{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7JfLUlawto_D"
   },
   "source": [
    "# Classifying the imbalanced data\n",
    "\n",
    "## Learning Objectives\n",
    "\n",
    "1. Examine the class label imbalance.\n",
    "2. Clean, split and normalize the data.\n",
    "3. Define the model and metrics.\n",
    "4. Build the model.\n",
    "5. Train the model.\n",
    "6. Evaluate metrics.\n",
    "7. Calculate class weights and train a model with class weights.\n",
    "8. Train on the oversampled data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mthoSGBAOoX-"
   },
   "source": [
    "Introduction\n",
    "In this tutorial, you will demonstrates how to classify a highly imbalanced dataset in which the number of examples in one class greatly outnumbers the examples in another. You will work with the [Credit Card Fraud Detection](https://www.kaggle.com/mlg-ulb/creditcardfraud) dataset hosted on Kaggle. The aim is to detect a mere 492 fraudulent transactions from 284,807 transactions in total. You will use [Keras](https://www.tensorflow.org/guide/keras/overview) to define the model and [class weights](https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/keras/Model) to help the model learn from the imbalanced data. .\n",
    "\n",
    "This tutorial contains complete code to:\n",
    "\n",
    "* Load a CSV file using Pandas.\n",
    "* Create train, validation, and test sets.\n",
    "* Define and train a model using Keras (including setting class weights).\n",
    "* Evaluate the model using various metrics (including precision and recall).\n",
    "* Try common techniques for dealing with imbalanced data like:\n",
    "    * Class weighting \n",
    "    * Oversampling\n",
    "\n",
    "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/imbalanced_data.ipynb) -- try to complete that notebook first before reviewing this solution notebook. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kRHmSyHxEIhN"
   },
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "JM7hDSNClfoK"
   },
   "outputs": [],
   "source": [
    "# Import necessary libraries.\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import os\n",
    "import tempfile\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import sklearn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "c8o1FHzD-_y_"
   },
   "outputs": [],
   "source": [
    "mpl.rcParams['figure.figsize'] = (12, 10)\n",
    "colors = plt.rcParams['axes.prop_cycle'].by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Z3iZVjziKHmX"
   },
   "source": [
    "## Data processing and exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4sA9WOcmzH2D"
   },
   "source": [
    "### Download the Kaggle Credit Card Fraud data set\n",
    "\n",
    "Pandas is a Python library with many helpful utilities for loading and working with structured data. It can be used to download CSVs into a Pandas [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html#pandas.DataFrame).\n",
    "\n",
    "Note: This dataset has been collected and analysed during a research collaboration of Worldline and the [Machine Learning Group](http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available [here](https://www.researchgate.net/project/Fraud-detection-5) and the page of the [DefeatFraud](https://mlg.ulb.ac.be/wordpress/portfolio_page/defeatfraud-assessment-and-validation-of-deep-feature-engineering-and-learning-solutions-for-fraud-detection/) project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "pR_SnbMArXr7"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
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       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
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       "  <tbody>\n",
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       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
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       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file = tf.keras.utils\n",
    "raw_df = pd.read_csv('https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv')\n",
    "raw_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "-fgdQgmwUFuj"
   },
   "outputs": [
    {
     "data": {
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       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
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       "      <th>count</th>\n",
       "      <td>284807.000000</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>2.848070e+05</td>\n",
       "      <td>284807.000000</td>\n",
       "      <td>284807.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>94813.859575</td>\n",
       "      <td>3.918649e-15</td>\n",
       "      <td>5.682686e-16</td>\n",
       "      <td>-8.761736e-15</td>\n",
       "      <td>2.811118e-15</td>\n",
       "      <td>-1.552103e-15</td>\n",
       "      <td>1.701640e-15</td>\n",
       "      <td>-3.662252e-16</td>\n",
       "      <td>-1.217809e-16</td>\n",
       "      <td>88.349619</td>\n",
       "      <td>0.001727</td>\n",
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       "      <th>std</th>\n",
       "      <td>47488.145955</td>\n",
       "      <td>1.958696e+00</td>\n",
       "      <td>1.651309e+00</td>\n",
       "      <td>1.516255e+00</td>\n",
       "      <td>1.415869e+00</td>\n",
       "      <td>1.380247e+00</td>\n",
       "      <td>4.822270e-01</td>\n",
       "      <td>4.036325e-01</td>\n",
       "      <td>3.300833e-01</td>\n",
       "      <td>250.120109</td>\n",
       "      <td>0.041527</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5.640751e+01</td>\n",
       "      <td>-7.271573e+01</td>\n",
       "      <td>-4.832559e+01</td>\n",
       "      <td>-5.683171e+00</td>\n",
       "      <td>-1.137433e+02</td>\n",
       "      <td>-2.604551e+00</td>\n",
       "      <td>-2.256568e+01</td>\n",
       "      <td>-1.543008e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>54201.500000</td>\n",
       "      <td>-9.203734e-01</td>\n",
       "      <td>-5.985499e-01</td>\n",
       "      <td>-8.903648e-01</td>\n",
       "      <td>-8.486401e-01</td>\n",
       "      <td>-6.915971e-01</td>\n",
       "      <td>-3.269839e-01</td>\n",
       "      <td>-7.083953e-02</td>\n",
       "      <td>-5.295979e-02</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>84692.000000</td>\n",
       "      <td>1.810880e-02</td>\n",
       "      <td>6.548556e-02</td>\n",
       "      <td>1.798463e-01</td>\n",
       "      <td>-1.984653e-02</td>\n",
       "      <td>-5.433583e-02</td>\n",
       "      <td>-5.213911e-02</td>\n",
       "      <td>1.342146e-03</td>\n",
       "      <td>1.124383e-02</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>139320.500000</td>\n",
       "      <td>1.315642e+00</td>\n",
       "      <td>8.037239e-01</td>\n",
       "      <td>1.027196e+00</td>\n",
       "      <td>7.433413e-01</td>\n",
       "      <td>6.119264e-01</td>\n",
       "      <td>2.409522e-01</td>\n",
       "      <td>9.104512e-02</td>\n",
       "      <td>7.827995e-02</td>\n",
       "      <td>77.165000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172792.000000</td>\n",
       "      <td>2.454930e+00</td>\n",
       "      <td>2.205773e+01</td>\n",
       "      <td>9.382558e+00</td>\n",
       "      <td>1.687534e+01</td>\n",
       "      <td>3.480167e+01</td>\n",
       "      <td>3.517346e+00</td>\n",
       "      <td>3.161220e+01</td>\n",
       "      <td>3.384781e+01</td>\n",
       "      <td>25691.160000</td>\n",
       "      <td>1.000000</td>\n",
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       "                Time            V1            V2            V3            V4  \\\n",
       "count  284807.000000  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05   \n",
       "mean    94813.859575  3.918649e-15  5.682686e-16 -8.761736e-15  2.811118e-15   \n",
       "std     47488.145955  1.958696e+00  1.651309e+00  1.516255e+00  1.415869e+00   \n",
       "min         0.000000 -5.640751e+01 -7.271573e+01 -4.832559e+01 -5.683171e+00   \n",
       "25%     54201.500000 -9.203734e-01 -5.985499e-01 -8.903648e-01 -8.486401e-01   \n",
       "50%     84692.000000  1.810880e-02  6.548556e-02  1.798463e-01 -1.984653e-02   \n",
       "75%    139320.500000  1.315642e+00  8.037239e-01  1.027196e+00  7.433413e-01   \n",
       "max    172792.000000  2.454930e+00  2.205773e+01  9.382558e+00  1.687534e+01   \n",
       "\n",
       "                 V5           V26           V27           V28         Amount  \\\n",
       "count  2.848070e+05  2.848070e+05  2.848070e+05  2.848070e+05  284807.000000   \n",
       "mean  -1.552103e-15  1.701640e-15 -3.662252e-16 -1.217809e-16      88.349619   \n",
       "std    1.380247e+00  4.822270e-01  4.036325e-01  3.300833e-01     250.120109   \n",
       "min   -1.137433e+02 -2.604551e+00 -2.256568e+01 -1.543008e+01       0.000000   \n",
       "25%   -6.915971e-01 -3.269839e-01 -7.083953e-02 -5.295979e-02       5.600000   \n",
       "50%   -5.433583e-02 -5.213911e-02  1.342146e-03  1.124383e-02      22.000000   \n",
       "75%    6.119264e-01  2.409522e-01  9.104512e-02  7.827995e-02      77.165000   \n",
       "max    3.480167e+01  3.517346e+00  3.161220e+01  3.384781e+01   25691.160000   \n",
       "\n",
       "               Class  \n",
       "count  284807.000000  \n",
       "mean        0.001727  \n",
       "std         0.041527  \n",
       "min         0.000000  \n",
       "25%         0.000000  \n",
       "50%         0.000000  \n",
       "75%         0.000000  \n",
       "max         1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_df[['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V26', 'V27', 'V28', 'Amount', 'Class']].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xWKB_CVZFLpB"
   },
   "source": [
    "### Examine the class label imbalance\n",
    "\n",
    "Let's look at the dataset imbalance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "HCJFrtuY2iLF"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Examples:\n",
      "    Total: 284807\n",
      "    Positive: 492 (0.17% of total)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "neg, pos = np.bincount(raw_df['Class'])\n",
    "total = neg + pos\n",
    "print('Examples:\\n    Total: {}\\n    Positive: {} ({:.2f}% of total)\\n'.format(\n",
    "    total, pos, 100 * pos / total))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KnLKFQDsCBUg"
   },
   "source": [
    "This shows the small fraction of positive samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6qox6ryyzwdr"
   },
   "source": [
    "### Clean, split and normalize the data\n",
    "\n",
    "The raw data has a few issues. First the `Time` and `Amount` columns are too variable to use directly. Drop the `Time` column (since it's not clear what it means) and take the log of the `Amount` column to reduce its range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Ef42jTuxEjnj"
   },
   "outputs": [],
   "source": [
    "# TODO 1\n",
    "cleaned_df = raw_df.copy()\n",
    "\n",
    "# You don't want the `Time` column.\n",
    "cleaned_df.pop('Time')\n",
    "\n",
    "# The `Amount` column covers a huge range. Convert to log-space.\n",
    "eps = 0.001 # 0 => 0.1¢\n",
    "cleaned_df['Log Ammount'] = np.log(cleaned_df.pop('Amount')+eps)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uSNgdQFFFQ6u"
   },
   "source": [
    "Split the dataset into train, validation, and test sets. The validation set is used during the model fitting to evaluate the loss and any metrics, however the model is not fit with this data. The test set is completely unused during the training phase and is only used at the end to evaluate how well the model generalizes to new data. This is especially important with imbalanced datasets where [overfitting](https://developers.google.com/machine-learning/crash-course/generalization/peril-of-overfitting) is a significant concern from the lack of training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "xfxhKg7Yr1-b"
   },
   "outputs": [],
   "source": [
    "# Use a utility from sklearn to split and shuffle your dataset.\n",
    "train_df, test_df = train_test_split(cleaned_df, test_size=0.2)\n",
    "train_df, val_df = train_test_split(train_df, test_size=0.2)\n",
    "\n",
    "# Form np arrays of labels and features.\n",
    "train_labels = np.array(train_df.pop('Class'))\n",
    "bool_train_labels = train_labels != 0\n",
    "val_labels = np.array(val_df.pop('Class'))\n",
    "test_labels = np.array(test_df.pop('Class'))\n",
    "\n",
    "train_features = np.array(train_df)\n",
    "val_features = np.array(val_df)\n",
    "test_features = np.array(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8a_Z_kBmr7Oh"
   },
   "source": [
    "Normalize the input features using the sklearn StandardScaler.\n",
    "This will set the mean to 0 and standard deviation to 1.\n",
    "\n",
    "Note: The `StandardScaler` is only fit using the `train_features` to be sure the model is not peeking at the validation or test sets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "IO-qEUmJ5JQg"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training labels shape: (182276,)\n",
      "Validation labels shape: (45569,)\n",
      "Test labels shape: (56962,)\n",
      "Training features shape: (182276, 29)\n",
      "Validation features shape: (45569, 29)\n",
      "Test features shape: (56962, 29)\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "train_features = scaler.fit_transform(train_features)\n",
    "\n",
    "val_features = scaler.transform(val_features)\n",
    "test_features = scaler.transform(test_features)\n",
    "\n",
    "train_features = np.clip(train_features, -5, 5)\n",
    "val_features = np.clip(val_features, -5, 5)\n",
    "test_features = np.clip(test_features, -5, 5)\n",
    "\n",
    "\n",
    "print('Training labels shape:', train_labels.shape)\n",
    "print('Validation labels shape:', val_labels.shape)\n",
    "print('Test labels shape:', test_labels.shape)\n",
    "\n",
    "print('Training features shape:', train_features.shape)\n",
    "print('Validation features shape:', val_features.shape)\n",
    "print('Test features shape:', test_features.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XF2nNfWKJ33w"
   },
   "source": [
    "Caution: If you want to deploy a model, it's critical that you preserve the preprocessing calculations. The easiest way to implement them as layers, and attach them to your model before export.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uQ7m9nqDC3W6"
   },
   "source": [
    "### Look at the data distribution\n",
    "\n",
    "Next compare the distributions of the positive and negative examples over a few features. Good questions to ask yourself at this point are:\n",
    "\n",
    "* Do these distributions make sense? \n",
    "    * Yes. You've normalized the input and these are mostly concentrated in the `+/- 2` range.\n",
    "* Can you see the difference between the distributions?\n",
    "    * Yes the positive examples contain a much higher rate of extreme values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "raK7hyjd_vf6"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n",
      "/opt/conda/lib/python3.7/site-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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5FYDrVPWN9X7mwgsv1Pvuu281hhdIOWl3bNpB0VPYBnDKcARv383kXCJaouGzVRHRHr2hquo+6OQM6hIAV4rIFQBiAPpF5Kuq+s4OjqktBmImfv/CYTx4OIs7np3Da09L4ZxNsU4Pi4ioq3RsBrVgED02gyIiagJnUDX2QcdvMyciIqqm0zdJAABU9Q4Ad3R4GEREFCKcQRERUSixQBERUSixQBERUSixQBERUSixQBERUSixQBERUSixQBERUSixQBERdYlkMtnpIawqFigioi6RTqc7PYRVxQJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFREShxAJFRNQlDMPA1tHtnR7GqmGBIiLqEp7nYfzgWKeHsWpYoIiIKJRYoIiIKJRYoIiIKJS6rkCpaqeHQEREq6CrCpTjKXKOskgREa0BXVWgAGC24GEq58H1WKiIiHpZ1xUoACh6iomsiyxnU0REPasrC1RZuuBhMudyNkVE1IO6ukABgOMBE1kXmaJfpFioiIh6Q9cXqLK5oodM0ev0MIiIqE16pkABgMfJExFRz7A6PQAiImqMYZrYvGWk08NYNT01gyIi6mWe6+LQ2IFOD2PVsEAREVEosUAREVEosUAREVEosUAREVEosUAREVEosUAREVEosUAREVEo9VSBKrqKdMFlP74VpqqYK7iYzbvwQrKvi65iOuei6IZjPEQUXE91knAUcBwg77jojxmImD1Vf0PB8fxCUK4DOcdFf9RA1OrMvvaLpYeM4w8o77qIWYJUxICIdGRMRNQePVWgyjwAUzkPMUuRjBgweKAKTFVLDXkXzlAUwHTeQ8RR9EdXd18XXcV03l3SgzHnKPKOiwGepBB1tZ4sUGXlA1Unz/B7Qa1CUKngKiYyLlJRA1FTVnT24qkiXfCQc2oPSOGfpERNRWqVCycRtUdPFyigs2f43U5LhSBbpxAs+H4AM3kPtgH0R02YRvv3dcH1MJ3z0OiVpryrKGR4kkLUjdbMO7Z8hp8t8iaKRhRcxUTWbbg4VSp6wMmsi0wb97Wn/rWvqSaKU1n5JGUqF56bOohaYRj+tdWto9s7PZRVsWYKFOAfqFo54K41Oad8MG99GwogW2zPvlb1Ty7yAe/QK7gKx2PqMnUvz/Nw1efvwvjBsU4PZVWsqQIFAKZI02fga43bpmBi02jPvtbSn3YwZWWvjxFR+6y5AkVERN2BBYqIiEKJBYqIiEKpYwVKREZF5HYReVREHhGRD3VqLEREFD6d/ByUA+D/VNX7RSQF4Fci8iNV3dfBMRERUUh0bAalqodV9f7Sf88CeBTA1k6Nh4iIwiUU16BEZCeAFwG4p8q/vV9E7hOR+yZOnFj1sRERdVLlMbDTY1ltHS9QIpIE8E0Af6yqM4v/XVW/oKoXquqF69avX/0BEhF1UOUxsNNjWW0dLVAiYsMvTl9T1X/r5FiIiChcOnkXnwD4JwCPqupfd2ocREQUTp2cQV0C4F0AXi0iD5b+XLHSD6oA2OhmOe1r8NqWfd3G3nkKsBcfUZfo2G3mqnonmqwV7YjKKLiKmbyHVNSAAOzLVqF84HaDdImtYJV68QXZw6oKR/1tBB2VIX6tkxWIASFaFSK46QN7AMPC1tHtODR2oNMjWlFdlQdlCDAcNxdEjreinBHkh+uxSAH+bMf1/OIdsGk4DPHzoCJm6/tVVaHAssGEjeqzBAnGwFO3U8VVn78LAPxC1eO6qkAB/ln5cNxEtughHSDOoRyuFzEF/Wt4NtXuQhC3BMmAhUBVUXAVswUvUOQHAJilYmkHKJZE1BldV6AAv5D0RUxELT+K3AkQD1EOMkxGDMSstVWkPPXzkWbywQuBIcBAwEJQLpazeS9w9hMAJGxBn81ZE1G36soCVWYagqGYiZzjIV3Qlq9RKIDZgoec459tG9K7hap8namdhaDPFiQCFgJVRd7xZ01BR2SVIuctXmsi6mpdXaAAv5DEbRMRUzEV8NpU0QMmsi5SPTyb8hQoeorZfLBCYAAw2lAIPFV46i8xFgIWS0P8JUbOmoh6Q9cXqDLTEMRtA+lC8DjYnOMhapk9eTt6utCeWVPEEqTacNOBp8Bk1m3Lje3r4iYLE1EP6XirIyIiompYoIiIKJRYoIiIKJRYoIiIKJRYoIiIKJRYoIiIKJR65jZzIqKeV24WC6yJhrEsUERE3aKiWSzQ+w1jucRHRESh1HMFin0E6vNUQxfY166WeUW3Pc8tW/TgtWE7rqdtydZS1baMR7U94wkjr037iMKlp5b44pYgZpmBoiMsA0hFe6/NkariZNbFkVkHANAfMxC1Wjs/iZh+pEY7mKWMr6yjLbep8lQxk/NwLO0gYQu2DURa6qrueoqHj+bw6PE8khEDl+zow3C8+beIqmIq6+Jw2t/X2/pt9MfMprcDlAM2XXgarDu737Xe7/wfK0WitCMAtNNUFXnX7y0JAMmIIGaxF2Ov6KkCJSIQoNTstfkYiYRtoM+W+W31irzjYWy6iLzzfMf36ZyHqKlIxRo/UJX3bdSStu2f8nbiFhA1TczkPRQb/KXNd0CvaHw7V1Q8OZHH5qSFoSZ6852Yc/DzA3PIlfbRbMHDj55K4/R1UZy3OQazwWlewVUcnC4gW3x+X49NF5HIutjWb8NqsHB6qktOtOaKipzjYiDWeINeVUWm6GGuIjst5yjyjov+aOsnKWHgeorZvIvK85rZgr+P+qNmw78zCq+eKlBlIgLb8JuHpgsessvMpizDzzLqtZgNVcXxOQfH56o3Y827isKci1TMQGyZA1XUFKRWMNhRRGAKMBgzkCvNpur91tzyjMBdGgXvKXA47WAy52K030akznNzPMUDh7N45mRhSSd8V4EnJ/LYP13AJdsT2Jio/XZR9XPFjqadJeMpB0I+MZHHlqSFwWUKZ97xMFOj27yrwMms21BCsONpzfRpBTCd9xBxFP3R7ppNqWrdiJ1yKkEyIohzNtXVerJAAc8fRMtBhDP5pW9UAZCIGIi3cUYQFrmiP2squPVzshTATM5DzvSQqnLWaYg/a4qYq7OPRAQxC4ha/mxqcQSHqvppyoX6Jx2qQLaoeHKigE1JE+v6rCXjP5Iu4q4DGRRdrRnT4pa2c/szaewctPHikb4ly4fVZqjVeAqMz/qFc9tABJFF2/FUG87oyjiKnOtWDYlUVcwVPWQaSJwuB3amogaiq/Q7DsL1Gg8pTVfMppgN1p16tkCViQgsQzEcNxe8aW1DMBA1ID02a/JUcSztYCLTXIRFwQVOZvyzzljpOkf5WsVKzZpqKS/VDkSN+esLitJ1lJwLt4lLVQrgaNrFZNbD6KCNmGWg4CruO5TB2HSx4fwwV4Hnpoo4ODODPdv7sCVlLztDrTWeTFHx5Al/GXK4z59N5Ryv6YwuT4HJnItYRfRJ0fUP4M0sbSuAmbwHu5TvFcalsWpLlY1wPH/GyXTl7tTzBQp4/uCasP3ZlOvpqs0IVlO26OHAVAGOt3TZqxH+tRdFwXWxfTDS8X0kIoiaQKTPxKHpIqbzrd1EofCXM5+eKEBEse94AY6nTcfcuwq4ruKnz81hdMDGurgZaF8fSTuYyrkYipto8Z4eAM9fU7JN/0SjVcXSwXwgatRdFl1trhc8jLSV63fUeeF5Fa6C8nWOTh94V8rh2SKKLR4wK/XHzNDsIxGBKlouTpUUwN5jeRTc5otTJVf9jQXd1woAgoZvClluW0GKU+V2WrkDciXNFb1AxanMVWCuDYGmtHrWxAyqUhgOutQchX+9MEyfcuHLiGjlrakZFBERdQ8WKCIiCqU1t8RHRNS1KruZA4Cx9OMTZSPbRru+0zkLFBFRt1jUzbyeXuh0ziU+IiIKJRYoIiIKJRYoIiIKJRYoIiIKJRYoIiIKJRaoEGhXwm2YOi2Uhe65tWlDYdzXYRO25GbqPl1XoHrtRe9HVQd/Xo6n6LOkLbHX6VLQY5BtqSocT5Fz/P8PQlUhCDae8nYGYsFf8gaA6ZzbltdiwVVoG37/7dSW3oAVr+ugzy1qStsSrqMWe1R1k676HFSulJ6ajBgwurwjsWopubWU/1OOOmg2NFHVzzJ69FgOe4/lYZuCHQM2opa0HEKXdRSPn8hjJGWhP2Y2vZ1yLMbPD2SQLnjYvTGKszfGYDb53FxPUXAVPz+QwbE5BwNRA9v67ZaCJctZS6ZhYCgmmM67fmFoait+T8DhPhObk7U/INkM1wMmMq6fW2Z3tlekAOiPGoiYrRfxcjEqB4Va5QgPaMvPLWoZGDZkSXpuM8IcJUK1dVWBUgUOTBeRihrYWjpQdVMSaJmqLsg5AipSQG0Dcdv/2nJvaMdTpPMe7jwwh5lSt++Cq3jyZAHDcRMjSavlvCtPgYMzDhI5D6P9Ngxj+X1dng0+MJ7FkycL81/feyyPA9NFXLI9gVTUWDbuoFx0nzmZxwOHc/OdrKfzHtIn8tjWbyPVYAqsql/kpkuzQsDv1r0ubmKu4CHTYM6FIYBl+MU/brd34aEcMZ9zUDohWP1CVc7+CvJ+UlUUPcVMxb4u5zH12YJEab+18txMQzAYt5rOzRLAzzhjsm5X6qoCVTab9/BEgDP8TinPmqolxZalix5ybv2zznIheOhwFo9PFKq+WU9mXczmXWwvHVBb3UdzpajyTUkLQ/Ha+9rxFCczDu4ay1RNcp3Je/jBk7M4fV0E52+J1zy5cD1F1vHw8/0ZTGSX5ke4CuyfLiIVMTA6YNedlXmqmM5VT6gVESSjJmK2H4vu1ZlNCYANfSY2Jtoza6qlWDGbiq/SbMoQ/7W2ON23GeXXdbrgIVej4GeKfmaVn27b+mwqZvnpzo0kD0dM6bo4e1qoKwsUUHGGn/WwbcCG2cAZfiep+tdj0oXlz/7qnXU6nmIq6+LnY5lls22KHvD0ZBGDMQNbU60tjQH+vj4864frjQ5EYFXsa08VrgfceyiD56aKdbejAJ6YKODQjIM92/swFH8+PK48a3rsuL9UudxlkNmCh8dO5LE1ZWFg0UlKeYY6nVt+X1uGYDhuVk1rNUrZYdsH/CTe1ZKumE01uyzajHg5MTngrKngKmYL3rK/M7eUAFx+XKC152aIYCBmIu94mKkymyovVUZDFLpIrZEwXZxdzu7zLtCbbv35kq8LgM0p/wx/tePJ6ynvW0/9GUQrF59NAQZiZukmAeBX41k8M1lY9ueqbWd0wA68jCMANiYsrOsz4QE4mi7i7rHssmez1ZwyFMEFI3GI+DO1O/fPtRRMmLAF2wciKF86mcrVnqHW43iKmfzzkfKbkxbWlyLZO6XPFiQCFpHFDAEGomagYMLF11BbGUN/1IBdOkFp9fl5qgtmbu1YquyAhgcrhuHfVdMIwwI8p1uaxlbdBz1RoMo2J/0DZ5gK1EyLb+DFnj2Zx/isg2yQbHAAuwZtpKJm4PGUz14Pp51A24lZgo0JC2PTxUC3bguAbf0WCm7AlFtVRE1BMuDNAu0Ut/zxBH1dt/N6zEzORd7VwLfbD8UM2G3Yz8XSWIIsVXZQ4wVKRBttFlt20wf2hOou0Rqq7oOuXeKrph23x7ZbO4oTAByadWqu7zfDbdM+mit6ODoXrDgB/p2ZB6brLw02QkvbCnwQF/Gvk4ToQNeul7WIf0dcO07g2lGcgPZ9nixsMfXUHuE4RSQiIlqEBYqIiEKJBYqIiEKJBYqIiEKJBYqIiEKJBYqIiEKJBYqIiEKJBYqIiEKJBYqIiEKpZwqUqmIi4+DZyULgYLu842E27wbeTrrg4eB0ATmnxRCbCqcORTAcD96iqOgBc4VgYXuqinTBbUuHg7zjYTrnBu5wcWImi/9x51MYm0gHHs/tz6bx9Mnm+x1WKrouvv6zR/Dte56A6wX7/berS4IqkM4H39cAkIwYCNqLVQCYIWlLRuHUE62OHE8xlfObfIq4GJ91cO6mGAZizR3QvVLvvHKj0ZzjIhU1/ETPJt5IniqeOVnAgekiPAUmswVsTAaLa9icsrAxaeH4nIPHTuTRbM0rB7a5CqQLipzjYiBmLpvNtFje8TA2XUQuYAuncoBgztFSmyIXqYggbjfXisf1FD999BDu2DcO1/Nw99PH8Zqzt+DKC3Y03eNt/1QBd49l4CrwyPE8dg7auOy0FPqazH96/NAEPvXNX2ByLgcB8IMHnsFH3nYxdmwYaGo78w1Vm3z91aIAci6Qy7pI2s3v60oxSxCzTGRLHfqbVY7CEJQSk1moqIqazWJFZDuAY6qaE//V8x4ALwawD8A/qGrwRmxNWtwsVkudjBfHJAD+m3skZeH0ddFlUzSrBQhWaiaNczrn4uGjORRcXTDDEAFsQ7BjMFjgXTneYt+xHI5nluYlLSYAUhEDUav6QS4Z8SM9ljtAqCpOZBwcSbuB+6flHA/TOf+gVrktQWlfN1g4D0/O4ca7nsRMtoBCRcWOWgYSURsf+M0zceqm/mW3kyl6uHvMT+2tLPym+EF5rzklgbM2RJfdR7migy/f9hC+f/8zKDjP/25EANs08fY9Z+Kal++G1UDhjFmCVLT1SIpGlNNumz1JqVSOdm+0W78ALZ309biV6WZeVupq3i4r1B29uW7mIrIXwEWqmhGRTwM4FcC3ALwaAFT13wUekcjlAP4GgAngH1X1U/W+v7JAFV1/1lQvaK6cgnrOpiiG49Uni24pYqG4zEmgAEhEBPEazTZdT/HkhN9xvN77VACs6zOxOWUFigRwPcVk1sW+4/ma0RKNBLYJ/P00GKsdv5BzPByYKvpFt+URl2aopQDBeuGAgF84+2oUzqLr4baHx/CLJ4/CcWtnPtmmgUtO34jfvmgXovbS2bSq4umTBdw3noXnoeZzsw1gc9LGFWcka3aCf+i5Y/j0v/0Cc7ki8k71E4eYbWIoGcNH37YHp48MV/0eU8oFevViYxJ27X3dqEbyzqKmX3TDFIkTEivazbzdVqg7etMFap+qnl36718BeImqeqW//1pVzws0GhETwBMAXgvgIIB7AVyjqvtq/czu8y7Qr//wTswWvKqprbUY4mcYvWB9dL5Ltaqf3DpXaK4rs2X4WTqVs6mTWQd7j+bheNrQdRkpbWf7YASJSOuzqedD/vI4UhF70epZasJeGGDnqeLYnIPjc8FmTeV9PZv3t9LItgSAafiFs/IMf/+JWXz9rieRzTsouMuXy4hlIGab+P1XnYmzRgbnv54uuPj5/gwmc25Dy6WG+MXjlbsSOG9TbH4fzeWL+PwP7sdPHxmrWZgWi1omXn/BqXjPq89F1H7+xCk+v//Le2D1mKVk3XZkRC1OjDbEn8XbpnRbTtNqYYFqIW5jTERerao/AfAcgFEA+0VkXZsGdBGAp1T1GQAQka8DeDP8JcSqFIrjGbfpGa6nwLG0gxMZB7s3xjAcNzGdb+zAtJjjARNZFwlbEDEFj58o4Nhc/VnTYgr/ZoVnThYwFDexJWU1tHy4mIjAEuCsDVFs7bew92gegF+cWjkQZIr+tanBmImi58dgOF6wWAXXU0znXRSbzGlS+Pv6RMa/XmKJ4ge/PoAHnjuBYgOFqazgeCg4Hv7fH+3Di3eswzUvPQUHZl08eDgHD2j4teSp/+d/PzuHvUfzeOMZKTxx8Cg++617kCs6C5YYl5N3XHz//qfxs31j+MhvXYzzdm3AwAqn5y6nnHbbZ7UekCgiEAADUWN+yTxaTu0FZ03UvHoF6n0A/oeIXA9gGsCDIvIAgCEA/7ENj70VwFjF3w8C+I3F3yQi7wfwfgDYODLa8p1jHgDPA56cyOPU4UjwwLa8h8dP5OsuDS1HAUxmXQzHTfRFgFbPmk1DMBgzccqQjRMZt+XnpvAPVOOzxapR2s1yPMVE6TpZkG1N51z88+0PI190mypOlQqOh189dwJ5K4F1/Um0eo9H0QOOph184n/dj/3P7V9wrakZ+aKLfDGLb939GF7xgk2BrgO1U8ZRxG1/RtUqEUHUBKJ95vzfqXWVx8C1pl6B+lMAfwZgEsDpAL6M0lJceakvoGqv2iWHDVX9AoAvAMAZ57448LzSNgWqQND3jOtpoOJUpgCiliDoko6IINuGwD7An7m0YwLvegqR4IF7RddDtuAEvj266Cr6YjEEzX1UANNTMy0Xp0rb16cCb6Pd2lErRYR357VJ5TFQRMKXyrqC6l0AeRLAZwDcAmAPgKdV9Z42FSfAL3ajFX/fBmC8Tdsmog5jcaKgahYoVf0bVb0YwCsBnATwJRF5VEQ+ISJntOGx7wVwuojsEpEIgKsBfKcN2yUioh6w7C1kqrpfVT+tqi8CcC2AtwJ4NOgDlz5H9UEAPyxt719V9ZGg2yUiot6wbCcJEbEBXA5/hnMpgP8N4C/a8eCqegv8JUQiIqIFahYoEXktgGsAvAHALwF8HcD7VXVulcZGRERrWL0Z1McA/AuA61T15CqNh4iICECdAqWqv7maAyEiIqrUE93MiYjWBBHc9IE9nR2DsTSVYYUayLJAERF1DVV0uhdfNStVNHsmsJCIiHrLmitQ7WhzBPjbaFfPEU/Rlu7A7UonbVcDABFpS+quIY03dV2O4wZvTwQAYhpt6TeeKwZPbl4JK9Ctmqhpa6pAGQL02QYSAQIDAcDzFEUXMNoQeW0IMJHxg/JaPSj4oXGKgZiBmCmBDpyqCq8NVcXfTvCuWI7rYSZbQCJqA2i9T5xpCCzTxPF0AU6Q56cenGIR7tQReJkpIEDnL9Mw8MREEb84kEHR1dAUhcmcC7dNJ01EQayJa1DVggvjdmNBhZXK6aHjsw4ms8HPxOO2IFWKNpjMuohbgmSTKaqqioLrR9WrAoNxE3nHw1SVxNrltuOq3zk8YJo7PFVM5RZmAkkTYykNCEXPwy8eO4gHnz1e+eWmiQgGBgawYeMmGIaBiayHuCVIRZrLy3KdIqZPHMED//sHyGXS/rajCUTXb4dhWvAaPDWwLROxaASXXHAe1g0N4FeH83h6sog3nJnCcNwKlMnUDo4HnMy66LNl/mSOffWoE3q+QNWKfi9HVNSLeq/kqWKu4GFsuthSjlRZvQTbrKPIuy76owZss/5BYT4cblEhAPzI8w0JwWzeQ7aB1t1aem6ZgG2+y6mq1aI6Fke713skx/VwYiaD79//DGazharb8fOF6ndKNw2BYZoYGdmKvr6+Bf/m72vFYNSAZUj9Zc3SrOnhu27D+LOPL/yn/Bxy448hMjQCIzEESO1ptQAwTANnnboDu884FUbFFHwq5+Frv57GeZtieMWuBEwBjA5HcGSKirzjlmLh2ZmcVl/PFihD/MjzczfFMBCrHtMtIohZfvDg4hTQsvKs6eB0EdP54EtWCbt+IJyn/sEqVppNVQt6U1XknfqF1RDBQMxE3FVM5fyQxyVFQxWO5xeUoLMm1/Mfp5EZaa2HUlUUXQ937D2Axw5OLL+N0oaqFTwRwdDQMNatX7+gEFTyFDiZ8xA1gf6oAVNkyXZcp4iJ8QP49Z0/QiGfrTVwFE4egqRPIrp+BwzLXjKbsk0TiUQcL7vgPAz0J2s+r18fzeHpyQKuOCOJTUm747OpcpBhvBQ8CHA2RaunJwuUIcD2ARunDEcaSpY1pDSbcrwFZ/+e+kXg4HQx0AG8Vnx5PbmK2VTEfD5fRxWYznsoNjigiCnY0GdituAhU3z+Z1QVswUPuTbMmrJFxUyhteJdLi6O62L8ZBq3PvgsMnlnuR/zH7vKdkxDYNk2Rka2IhaLNbSdvAucyHgYjBmwjdIB2HNRLBbw4M9uxbGxZxobTyGL3PjjsAc3wUyth4h/IiKG4NwXnIYzT93R0OsxXfDwr3tn8IL1UVx6aiIUUekLZvcGixStjp4qUBEDiFiCczbGkIxWnzXVE7UMrDP960E5R3FwuojZFg+8ZQIgGRH02c3HaKsC0zkPEVOQihrIO4p0C+MREfRHTcQtxYmMg2Jp1hTkXgHPU3gApnJuoCXPfNGZnzU9fWSqpW2Un4Zpmli3bhjDw+ua39cAJnMeIiYQ1zwmxvdj7y9+AqdYWPZnF2+pOHUEztwkBkbPQqo/hT0vfiGSib7lf3SRx07ksX+qgDef1Y8tqaUfjlxtlbP7FGdTtAp6qkBtTtk4bV1js6ZaDBGYInhyIhfowFu2MWEGfhMX3Oej04OwTUHR0/kbKILIuX6xDHrD30y2gG/c9RgKbdjZp59+ehv2NfDrW7+JuakTgbajxTxesHUIu8+/MNB2so7iV+NZXH56Cnbz51wrIucokhF0fFZHvW9N3WZORETdgwWKiIhCiQWKiIhCqaeuQRER9bQwdDOvptThvN1dzVmgiIi6RUi7mZe1u3hyiY+IiEKJBYqIiEKJBYqIiEKJBYqIiEKppwqUqsJtU5ZRuxpJtyuMztP25AW5nrZlTK6nwXKVSvzn1KagxbZsBTCt9tw7ZBrtCTUE0JbXdTtVa6xM1G49dRffwRkHh9MOztoQw6Zka0/N8fxcpDPXR3Ek7eD4XLAWQ8czHmKWoj9qtNQaRtVvKTRXVNgGMNBEw9nF23l2soB7Dmb9ZrqDESQirZ2fjM8U8L3HppFzPFywNYFdw9GWWgzN5Iq485kpSN8g7EIWxVympfGYhoEzd4xgQ8IstXLSltOOE7bgkjdchbmpCfzqju9hbnqype288Owz8eZXXQjDtDE2XWyphyIAxEvd9u8ay+D0dRFs67c72v/O9RRPTuRxcMbBlqSFl472IR4wAJSoFumm1Mwzzn2x/t23frrs9xkCDMVMnL0xiqjV2JtHVZEp+oWgzFNF0VXsnyoG6vpdPpwMxAzEGhwPABRLURneoqiMZMQPkmv0QDVX8HDXgTlMZJ9v7CoAhuIGtqTsBTlZ9RRcDz99No1Hj2Xnt2MZwHCfhZduTyIRaaxZnKeKR8ZncO9zU/BKcSYChXoe8plZqNtYN3MAGB5I4qxd22CbJiB+mrCripm8n/fUKEuAwZhRsS8UruPg6Yfvw1O//iW0weTcVDKB33nblTh15w5EIvb8853JuTg44zTcu1AAbElZGO4z509sTAESEQPnbIqhrwNF4WTGwd5jeThe+Xfmd+m/YCSOU4YibBzbuoZ3nIho2G8zb7GmVN0HPVmggOeDAc9YH8FIqv5ZZ9H103WrHc/KwYAn5lwcTTstn5mXxxQxBQOx+rOpchRGZTzG4u2Y4qfn1ptNqSqemMjjgcM5eLo03M8Qf413dNBGapnu7/sn8/jBE9Mourok88kQPzzwvC19OGN9rO6+nswU8JPHjmMm51SPDFGFV8yhkJ2rOx7bMvGCnVsx1J+smvfkR3gophro2p6KCOJW9XRdz3WQm0vjV7d/FzMnj1f56ee95PwX4u1XXo6IbVcdk+spxqaLmFkmV6zPFuwYjMCqEWthCHDKUAQ7BldnNlV0FY+fyOPYXPUCa5Vei3u29yHZ4EkKLcACtdYKVJkhQCpq4JyNsSVLEc0kyXqqcDzgwFShZuFolABIRQVxa+ksqFAnYLCahO0HyS3ezkzexZ37M5jJLx+HYQDojxkY6beXFLyc4+H2p2fw1ER+2e1YBtAfNXHxjhT6F4VEep7igYNTeHBsBp5XfwnOAOB5HgqZWXhuccm/bxwawBk7R2CZBpZ7b/vFXqsmC1sGMBg1GphBKlzHxdjjD+HRX90Jz1247Ds00I93X/VWbN2yZX7WVEu9ZGZDgK39FgZi5rLLwaYAMcsP5GwlWqZRx+ccPHKs+glOpfIJ4XmbYzhzfWtLvmsYC9RaLVDA8/Hgpw1HMDrgn3UWXcV03m06LsJTxWTGxXjaQZBdJ8D8NSXTkPlwxEYi2hdvpzJC3lPFvmN5PHw0t2RpsB4D/j7aNmDPJxA/dSKHW5+cgVsqzs2MZ/emOM7aFIchghPpPG579DgyRbfhoEUAgCrUKSCfSQNQRGwLu0/ZhlSir2ZKbi2Op5jMPT+bGogKomb1WVPN4XgO8tks7r/jFkweG4cI8PLfeAne9LpLYVsmpIkxuZ7i0ExxPvokGTGwfXDpCcJymg3nbFTBVew7lsPJbHPvEbN0Qviy7YklJylUEwvUWi5QZYYACdvA6esjgbKePFW4HvDURL6hiPN6BEDcArKlyy5Bfhuu5+H+8Rzmil7Lz88QwBZg3/EsDk0XWn5+luFf4E9ZHp46PtfyXWgGAE89DMUEp23dBNMUtHq/Xjn9N2ZL1Xj3RrlOEekjz2LPmVuxcf0wbLv+rKkWTxXZogfXA5It3kQD+L+ziCm4cGu8qWuctRyZLeLR4/mmTnAWMwW4YCSGU1u8gWaNYYGqsQ966i6+5XjqzxIKrgYONXTQ+KyiHgWQafyegLoePJLH9DLXN5bjKfD4RA77JwuBiqXjAZOZIg7nsoFmmh78pNwzRjcGPtCJCPoi/jaCPDfTsvHyC87BpmSwa0CGSMM3ltTjqV+g7DZ8NkJVsfdYPvB2XAU2Jjp7x2FPCmuz2DKj8eTnRhrLrqkCBfgFCu346I0qypsKi3bNhj2073kZELht2JqWTi7CwjIav4tyNYTttQigbZ8lpAohbxbbjEYKLT/AQEREocQCRUREocQCRUREocQCRUREocQCRUREocQCRUREocQCRUREocQCRUREocQCRUREobQ2C1SbPuEetk/uh1Hb9hG7EnSddv3uu6lfKLVXVxUo9bwlUQfNyjnalmOdZQritgRu52KI39W8HdvZmrIRtFeoIcCmhI2YJQiaiWcaBgzDD7ULtB0BRL3Az81s0742xY+haAe/8XDwA3Cm6MH1tC0H8+G42ZZ9dHCmGHg85Tw21fY8N+ouXdWLb/+zz2Df3odw+plnIRqLNfWzhgB9toFzN8UQtwXpgtdySq7AfxNvGu3Dgekinj5ZaDi/qXIbhgCnr4tga7+NiYyLR47l4HjNn3n6OUI2XrkzgeNzDr720BSmsh6KTR74bAPYNRTBb58zgIKr+Mvbj+BXhzJN7ydT/AK+baAPETOJgxOzODKdaTraxDYEtiV414VbcM6WJJ6aKOBXh7PwPL9fYKMq41a2D9o4mXXx8NE8XE+rhlTWY4q/jy4YiUPEz91yW/idlTuZH5gqwlM/5iTVQkdzA37n+O2DEUznPPTZgkSVfLBGiQhetCWGI2kHj7XY0dwQYMegjaG4icmci4GoWQq1bG5Mqoqco0gXPIgA/VEDdo0QR+pNXRW3YUTiGt18Gl556WX49//xw4hGozDN+jW2WhZUWSuZUDFLkFp0AMgWPew9msNsYfn0VsB/Aw9EDezeFFsQj+B4fnLp0XRj0eCGAFHTD62rzN5xPcXP9s/htqfTDRU8UwDbFLxt9wDO3bSw8N/+zCz+8idHkHc8FJapClL6n40JG+sWdbKeyxXx5JEpFF2voRmDbQpevDWFt79oE+L2888tU/Rw14EMTmSchrrJmwKkIgbO2bwwJt31FE+eyOPgbGP72hIgYgletiOBjYnnX3NaKjTpJkIsF2dBlc1nQpVftMsQAOv7TGxKWgsKm2kAA1ETZsCDecHxsO94vuFMKFOAeOkkMBFZON1N2Ab6bH8sy41J1Y+Un8kvPcmKWaWAzga200Uaj9swDA0UDxAii7qZd38eVLlAAcDA4BA+9OGP4YUvurDmbKpemm5Zo6m6flHxQwFrbWd8tognThRqnnWWZ00v2BDF5mTttvSTWRcPH83B8bTmgcEQYMeAjV11gupOZBzc+NAUjs85KNRYGbUN4KwNUbzl7IEFB/BKMzkXn/7pUfz0uXTN2ZQpQMQysHUgimiNtThPFYdPzuHQZLrmjDNiCmK2gfdcNIIzNiaqbkdVsX+qiHsO+rOyarOgcgDjmev9GWqtfT2Vc/HwkRwKbu3ZlCnAGeuiOH9LrGb6ruvpfHpxrVdSOU33wHQRbo3i2kiqrr8sLNg+YNd8XQNA3H7+YB6kFfxyqbr1TgIrlROXzTqzKVU//Thd52zIKJ10RJoMnAyx0OVBBch1alVvFaiyi/a8DH/ykf+MeDwO0/KD48qF4Mz1EWxJNZZJU3T9A0y1g1Sf1fiySd7x8MixPKZyC886DQHWxU2ctTGGSI0iV8n1FE+dLODQTHHJduKW4NzNcSQjy1+U8VRxz8EMvv9EekHBswx/9nXVuYM4Y3102e0AwD1jc/jPPz6MTMFDvrSjysfrTUkbQ32N7etswcFTR6aQK7oLZlO2KdizcwBvPncjIg1ccMo5Hn55MItDswsP+Kb4CcO7N0UbCvDzVPH0RAH7pxfuaz900cDLd/RhuG/51XB/ScpDurA00t71FGPTRcw0mNfVZwt2DEZgLZoFCYBNSRMb+hrL3TEEGIgZsIxgB/Oi68/uj80tnHGa4oct1jsJXKzPFiRK31seU3nWNF0q8o2ImoJUtCdmUyxQvVqgACCRSOLf/8l/wktf/krEolEMxUycvbH2mXwtqopM0cNcabnGFP+Mr9asqZ6jaQePHvfPOg0Bdm+MYUOi+Ut+0zl/NlUoFYRThyPYXucstZapnIubHp7CwekiAOD8LXG88cxU0/soU/Rww8+P4XuPz8DxFHHbwMhAFJEm74RQVRyZymBsYhamIeiPmnjvS7di53C8qe0A/sX4XxzIwPEUIsDZG6LYVGeGWsts3sVDR3Lzs8RzNkaxe1Os6etCnvonOwW39N85FwdnGltKrCQAtqQsDPeZMEUQs/xZU7O/M6C0NB01ShOp1g/mJzMO9h7L+/sawBnrIxhp8CSwUvm9ZRr+aDJFxVwL8c0Cf5Ukaja2fBhSLFC9XKDK/vxjf4r3Xvvb2NTf2IygFsdTFF1FzAp21llwFcfSDjYnLVgtFLkyTxWHZx0Mxc2ay3CNUFXsO55HMmJgx2Ck5e0AwP944CRufmwGfREz0D6KGB5GEoKX7hyEFeDWsYKrePhoDpuSVkMz1Fo8VczmPWxM+MtsQTx9Mo9jcy7mlrt4t4xdgzbO2hDFUDzYvrYMBN4G4M8GD6cdbOgzWyqWlaKmwNXg6dSpiCBmhStEsgksUGsh8n3y2GGs6wseoW0ZEuhgWRYxBdsG7MDbMUSwtT/4dkQEuzc2d/djLTuGohjqs+dndq3qi1j4jR2pwPs7YgpG27Svdw1F2vL7dzwELk6Af42tPxa8sLThbnYAgGkItrXh9Qhgfqk4qDZthkKmqz4HRUREawcLFBERhRILFBERhVJHCpSI/FcReUxEHhKR/yUig50YBxERhVenZlA/AnCOqr4QwBMAPtqhcRARUUh1pECp6q2qWu62eTeAbZ0YBxERhVcYrkH9OwDfr/WPIvJ+EblPRO5TL1gncyKiblN5DOz0WFbbin0OSkR+DGBzlX/6uKp+u/Q9HwfgAPhare2o6hcAfAHwP6i7AkMlIgqtymOgGIbe9IE9K/6YI9tGV/wxGrFiBUpVX1Pv30Xk3QDeCOBS7aZ2FkREnaKKlegk0YHOEQ3p1F18lwP4UwBXqmqmXduNbj4FDx7Jwwu4owuuIl1wA2/H8RSzeTdwIJ2ninTeRTHgx+X9zu0uskU38ItxQ5+J15ySQICuQgD8XnNb+63A2zEE2BywzVF5O+VO2UGdNhzBWRuCtd0C/GylTQkrcNBmtNz0OOB2DPHjM4LuIlXFXN5FPmifI6AtnT8ofDrSi09EngIQBTBR+tLdqvoHy/1crV58gxs24er/8Als2XEqYrEo+qMm3nhGCuubbM7qqS4IMhT4IWlBm84CQNIWxO3me4XlHQ8zeW++O3a1PKpGLM6+ssvRBy2+sVUVRU8xlfPwd/dM4LmpYlM/b5uCN56ZxIUjfTANQBVNdfuu1Gf7+wTwoy6OpR0cyzR/vTIZMbB9wJ4/8OYcXbDvGxW1BDsGbNimwFM/9uR7j882/dwStuDt5wzg1OEoLANwPWBsuoBME9lTgP863piwsC5hQuDvo1YDOyvzmIDWm7wWXL+Jbvn1GDEF/bHmAxvLTWfLb1H24mtNCGZQvdcsVsTAntf/Fl5z1e/BtiMQ4/lCYhnAi7fEcfFoX0MH4cWFoFLEFPQ3mHbqeIrpXPXYDlOAgZjZ0Nme3xHbq9rrTuBHKDTSQVxLRTdb42CUjAjiAZpslgvVHc/O4X/unUYjx6pThyO49oUDiNvGgn1Rzksamy42HEY4GDOXRFJ46jf73T9dbOggbAqwrb96oq2niumc11DPOL8QmNiQtBZEQPix7sBdB+Zw/3iuoYJ33uYY3np2P2xDFrx+PVVMZV0caTDUMm4LRgf82I7K51b+vc3kGw/Z9BNtFzZQbjYmw18NqF4c5zuTN9ikOV4qlkDXFqYyFqheK1AbRrbjmj/+cwxv3go7Un0ZxTL8mPc3nJHCllT15pb1CkGlyrb+1d4MjQYfAv6ZcV+N2VS9TKHFynk4tQpnoZRxtdwBqBwkF2SZpOj6Y/7vv5zAExOFqt8TswRvPSuF3ZviNZfQyge88VkHk9nas6BEOYyvzoHJU8WJORdH55ya+3IgamBbv103klxVkXf9QlVrO3FLsH3QnzXV+n04rmIq5+K7j8/iZI3nNhA1cNW5A9g2YNc8AfFK++jgdLFmsJ8hwKakhaF47eDD8nu/3glM+bnVKwTl7SwXNJh3PMzU2Ydltll/dl8+0TMETc+4QooFqlcKVHzrmfjNt74TL3/T1bAjEUCWn0VYBnDOxhheviMxn+1UPujMNrmEU21prJXo+GpZU+VU1mZWTKotQy5eqmxUvcLZqILj4ZeHsvjqr6cWPP7ZG6J4x7kDiJrS0IzWU0W2qDgwXVxw7c0y/FlTvVTWSv5MATiwaGnMMoDRfhuJSOPLSuWTmcrnVZnZ1EhwnpaiJe47lME9B7PzrxkB8Bvb4rjiDL+zu9HgPprNexifKS6YsSciBkb7bRhGYwfw8pgWB3ZWLp81uq+rRbX7mVjLnwRWEgCJyNJl8Wai47sIC1QvFKhoakj/0z/fgf7h9bBqzJpqKSfIvv6MFLam7KYLQaXymydmCtJFbWktvyxuCRK2IOco0k1eW6hUXob0k4Gbv25S1swyZC2Op8gWPfzDfZN4erKAd5zTj1OHIy2FGqoCh9MOJjIukhEDCbu1jC5PFZNZF+OzDgZjJkZSVt1ZU70xla+9+bOmCMwGC0Elx1Wkix6++9gMXAWufeEgNiTMlvaRp8ChGX82NZKy0F8nLr7edgBgrughU9SqqbfNbCvn+DcI+f/f2utRAJiG/3qMWoKBqNnS76wLsED1Qh7UhpHtGN40ArTwAnU8/8B5+zNpvO70VKClAf8isyLdchl4XtbRussrjSq4ihMt3BiwmKv+ks9AtPWZlGUIUlETf3DREKZyXulA0/y2RAQiwOakiYgh8FRbHpMhguG4iaGYCQRYGhIRREzB9gETyQavS1ZjmYIBw8Dvnj+EwXjry1Ui4l9Dq8jCanU7gL8k3mcjUIy6iCBmAROZ1osT4L/PHM+/OWSwhaJL3a+rCpSItFScKiWj5nwMO1UXMCR1nintCX4UESiCnzmXC147RK3a15qaGU/UkoaXK+sxRKABCni7tyMimCu0XpwqJSLB9zV1pzC0OqIexsPK6mnX0lfbltD4y6eAWKCIiCiUWKCIiCiUWKCIiCiUuuomCSKiNU0EK9HNPCzdyxdjgSIi6hbLdDMPweeZ2opLfEREFEosUEREFEosUEREFEosUEREFEpdVqCCX/zzVPkB91VSblHUi9p1HbpX9w/QvigMT9GWC/+qirzjBU64ptXTVQVqbnoSxVy25Z83xT+wBI1yL28r2Yb47Hbqs/w4kKDa9f61DT9JOPh2BNv6/aTbVrcm8PsvbklaiFkS6PcmAGK2INJV757Vt2PARtwK1v9QgLYUFL84+Z3+J7Iu5gpuT93t1qu66jbzySNj+Pyf/Dbe9X/9A5KDwzDtxiI3TPG7ae8Z7VvQ9blVlblJMUswm28scXWlVGZUtZpzBfgH8IFFGVVBiAj6IiaiVvM5V2WV+zoVNXAk7WAq6zb13ARAf8zASMqGaQiG+0xMZFwcTdcOMqy1nZgl2DZgz+dv1UtirscoZS3VCm7sBRHLwCnDEUxmXRyebX5fR0zB6KCNWIDuxap+8OfiUNK5oiLnuIGjZWhldVUelIgoAFiRKN74f/xnvPTNvws7Eqvb4dwUYPugjZds7Qt8MLAM/wBeLTqi4PppoS1GTLVE4Ee2x6pEtpfD7BotnH2WILFMQm0QzSQFA/VzqTIFD2PTBThe/SWy8qxpdMBGMmou+feC6+HgdBHZ4vJjEgCbUxaG42bVfd1MQGQ5obYHc41qKrqKg9MFZBxddnlUAGxKmljXZwXaR+VcqvQyXdXL2Vcd/H20LQ+qiz8H1f2BheUCVbb1jBfi3X/5jxjYuAVWJLbge00BbFPwsh192JwMNmuaT/esUggqqSpmW0iybUU5oHC5df7lzvDbEVDYjHIqbb101aS9NEm12naOlYIMq21JAAzFTWxOWXX3kZaCDI+knapLmwL/ALZtILLszLLg+jPFWitS7Z6hdhst/e4PzRSr72vxZ6ij/TYiAWdNfrJv47P2agnXq4gFqhcLFAAYpoXXvudP8Ju/80ewI1FADJgCnDocwYtH4oEPvNUi3pfTSgR8owRLI96XU+sMvx0R762qVjjrzVBryRY9jJVi4RX+/rEMYHQggr4mLhIVXcWhmQLmKmZ4hgAjKQsDsaWzplq0tK8Xh1CG4Cw9NBxP/QTgit+/IcDmpIWhKjPURlQex7JFP624FR2a3bJA9WqBKtu443T80d/+T2zYsg0v35nA+r5gl9cEQCpqIGq2FjHuH6hcZJ1Aw1ggagKpaOvJouUz/PL1j06vvZeXIQuu1lyqbISq4vicg+MZF+viJjYm68+a6pnOuRifKaIvYmBrv93yPiqW9jWwujPUbjKbd3FwuoiYLdjWv/wMtR6vYtbkBFxnj5iCwdjSJeEVxALV6wUKAD7wwT/GJz75/8CyghUn2/APKkFvk1VVHG9DDDsArIs3N7MgWkvmCh7mWpw1LWYZwHB8Ve8fa7xAGUb9i3iGBXj+WfHItlEcGjsQeHCrpOo+6Kq7+IiI1rRlmsVWWomu56uNn+QgIqJQYoEiIqJQYoEiIqJQYoEiIqJQYoEiIqJQYoEiIqJQYoEiIqJQYoEiIqJQ6rECJW3JeiIios7rmQI18oIXI/Gq9+Gf7p/Bc5OFlrdjtDmIcEOfiWTAZLu4JTCkPamiRL0obgvWxU3YbWgHZrKhb2h0fasjOxrHq9//59j92nfAjsaRdRTfeWwGpwxFcOmpScSbiJgtdzIG0JZuxuVtxC0gapqYyXsoNtHivByFYUp7xkPUqwzx45YHY0ZDGVC1lDv8Uzh0dYHaft4luPKj/x3x1AAMKzL/dccDnj5ZwP6pSbz2tCTOWF8/edec7+69MoVARGA2+eYpxzOUf56IluenXANRy1w2d6ySZYSjwz8t1JXdzKOJfrzujz6F0/ZcDjsar/szlgFs67fxutNTSFRZalvtQlArgrqs/EbhrIkoGFVF3vUjXeod5RoJyFxh7etmXsG0o3AKuZYHtcp6I27jtJdehjdc9zeIxhMQq7GkXEP8WdJvnpLA7o0xiEjHC0G1N0/SNhC3/bGwOBEFVz4hnM17yC86IWwljHSFtC0PqlKXZUN1f9zG0MhOXPmxv1921rSYp/6f25+Zw75jeVx73iCGSoFknSoEIoKoCUT6TGQKHuK2AYOzJqK2EpH58NG4q5gpeFBFoIBMWj1dVaAiif6mi1OlouffdJCKtBYr3W7lN09i9SOmidYUQwS2CfRHDFimBA4jpdXRVQWqHS8pU6Q07Q3PC5TFiWjliQgiFt9r3YT3UxIRUSixQBERUSixQBERUSixQBERUSixQBERUSixQBERUSixQBERUSixQBERUSh11Qd1iYjWNBHc9IE9DX3ryLbRFR7MylubBYqdG4i6hqqy20qZKsrNYrusGWxLum6JL+jL9OhcsfueNNEaVe5Grqo9fzCmpbrqWD0YMzEU94MFm2WbwLo+E+84ZxCWybMxorBTVRRcxUTGxcmsC8cDi9Qa01VLfIYIXn96Ek9M5PHA4dx8jEY9AsA0gFftTOJVuxJhyH4hojqqhXq6CkzmXMQtQTLCpOm1oqMzKBG5TkRURNY38TM4c30MbzqzHxv66s+mIiawOWnhP1y8HpeemmRxIgo5VUXO8WdN1RKns45iIuui6HHJby3o2AxKREYBvBbAgVZ+PhEx8JpTk3h2soBfHsoumE2VE3QvOy2FPdv7mP1CFHKqCk/9WVNxmWURT4GpnIdYaTYl4GyqV3Vyie+/AfgwgG+3ugERwSnDUYykbNx9MIMjaQeGAFtSNq46dwDD8a5awSRac8qzoKyjSBe8pn425ygKrotUxEDED8hmoeoxHTmCi8iVAA6p6q+Xe0GJyPsBvB8AttS4rz9mG3jVriRm8v4a9flb4nyhEnWBTNFD3lU4zdWmeZ4C03kPqVKEey+qPAauNStWoETkxwA2V/mnjwP4GIDLGtmOqn4BwBcAYPd5F9Sd++8aimBz0mJxIuoShQDFqVKVy1U9o/IYKCI9/EyXWrECpaqvqfZ1ETkXwC4A5dnTNgD3i8hFqnpkpcZDRETdZdWX+FT1YQAby38XkecAXKiqJ1Z7LEREFF69uWhLRERdr+O3uanqzk6PgYiIwqfjBYqIiBpj2/Z8N/Ne6Fa+HC7xERF1iRe+8IXzjXMPjbXU46CrsEAREVEosUAREVEosUAREVEosUAREVEo9UyBEgCZgos11QeEqMslSt3Ig6oWzUHdryduMxf4abkbkxajNYi6SMQ0sK5PMJv3m8a2os8WJGyDPTh7UFcXKAFgm4LRARtxu2cmg0RriiGCgZiJguthOuc1vApiCjAQM2ExiLRndW2BEgAbEiY2JNi9nKgXREwD6/sEswUPOad+mUrYgj7Omnpe1xUoARC1/FlTtEfzX4jWKhFBf9RE3FJM510sDte1DGAgasLkrGlN6LoCtSlpYV2fyTMnoh5mm4J1cRNzBQ+Z0mwqGRHELc6a1pKuKlAxW7A+0VVDJqIWiQiSURMxWyEAZ01rUFcd7fnyJFp7eBPE2sWLOEREFEosUEREFEosUEREFEosUEREFEosUEREFEosUEREFEosUEREFEosUEREFEosUEREFEosUEREFEosUEREFEosUEREFEqi2lrMcieIyHEA+zv08OsBnOjQY3cSn/fawue9+k6o6uWNfKOI/KDR7+0FXVWgOklE7lPVCzs9jtXG57228HlTmHCJj4iIQokFioiIQokFqnFf6PQAOoTPe23h86bQ4DUoIiIKJc6giIgolFigiIgolFigWiAi14mIisj6To9lNYjIfxWRx0TkIRH5XyIy2OkxrSQRuVxEHheRp0TkI50ez2oQkVERuV1EHhWRR0TkQ50e02oSEVNEHhCR73Z6LPQ8FqgmicgogNcCONDpsayiHwE4R1VfCOAJAB/t8HhWjIiYAP4/AK8HcDaAa0Tk7M6OalU4AP5PVT0LwEsB/OEaed5lHwLwaKcHQQuxQDXvvwH4MIA1c3eJqt6qqk7pr3cD2NbJ8aywiwA8parPqGoBwNcBvLnDY1pxqnpYVe8v/fcs/IP11s6OanWIyDYAbwDwj50eCy3EAtUEEbkSwCFV/XWnx9JB/w7A9zs9iBW0FcBYxd8PYo0cqMtEZCeAFwG4p8NDWS03wD/p9Do8DlrE6vQAwkZEfgxgc5V/+jiAjwG4bHVHtDrqPW9V/Xbpez4Ofynoa6s5tlUmVb62ZmbLIpIE8E0Af6yqM50ez0oTkTcCOKaqvxKRV3V4OLQIC9Qiqvqaal8XkXMB7ALwaxEB/GWu+0XkIlU9sopDXBG1nneZiLwbwBsBXKq9/eG5gwBGK/6+DcB4h8ayqkTEhl+cvqaq/9bp8aySSwBcKSJXAIgB6BeRr6rqOzs8LgI/qNsyEXkOwIWq2vOdn0XkcgB/DeCVqnq80+NZSSJiwb8R5FIAhwDcC+BaVX2kowNbYeKfdf0zgJOq+scdHk5HlGZQ16nqGzs8FCrhNShqxOcApAD8SEQeFJG/7/SAVkrpZpAPAvgh/BsF/rXXi1PJJQDeBeDVpd/xg6VZBVHHcAZFREShxBkUERGFEgsUERGFEgsUERGFEgsUERGFEgsUERGFEgsUrWkicoeIvG7R1/5YRP5ORNyKW66/06kxEq1VvM2c1jQR+QCAl6rqeyu+djeA/wTg+6qa7NjgiNY4Fiha00RkHYDHAGxT1XypUepPAewAMMsCRdQ5XOKjNU1VJwD8EsDlpS9dDeCmUr/BmIjcJyJ3i8hbOjVGorWKBYoIuBF+YULp/28s/fd2Vb0QwLUAbhCRUzsxOKK1igWKCPgWgEtF5MUA4hXBfeOl/38GwB3wM5KIaJWwQNGap6pp+AXoiyjNnkRkSESipf9eD7+Z6r5OjZFoLWIeFJHvRgD/hueX+s4C8HkR8eCfyH1KVVmgiFYR7+IjIqJQ4hIfERGFEgsUERGFEgsUERGFEgsUERGFEgsUERGFEgsUERGFEgsUERGF0v8PX0fdyqUu1XgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos_df = pd.DataFrame(train_features[ bool_train_labels], columns=train_df.columns)\n",
    "neg_df = pd.DataFrame(train_features[~bool_train_labels], columns=train_df.columns)\n",
    "\n",
    "sns.jointplot(pos_df['V5'], pos_df['V6'],\n",
    "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
    "plt.suptitle(\"Positive distribution\")\n",
    "\n",
    "sns.jointplot(neg_df['V5'], neg_df['V6'],\n",
    "              kind='hex', xlim=(-5,5), ylim=(-5,5))\n",
    "_ = plt.suptitle(\"Negative distribution\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qFK1u4JX16D8"
   },
   "source": [
    "## Define the model and metrics\n",
    "\n",
    "Define a function that creates a simple neural network with a densly connected hidden layer, a [dropout](https://developers.google.com/machine-learning/glossary/#dropout_regularization) layer to reduce overfitting, and an output sigmoid layer that returns the probability of a transaction being fraudulent: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "3JQDzUqT3UYG"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-08 16:12:42.220437: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "METRICS = [\n",
    "      keras.metrics.TruePositives(name='tp'),\n",
    "      keras.metrics.FalsePositives(name='fp'),\n",
    "      keras.metrics.TrueNegatives(name='tn'),\n",
    "      keras.metrics.FalseNegatives(name='fn'), \n",
    "      keras.metrics.BinaryAccuracy(name='accuracy'),\n",
    "      keras.metrics.Precision(name='precision'),\n",
    "      keras.metrics.Recall(name='recall'),\n",
    "      keras.metrics.AUC(name='auc'),\n",
    "      keras.metrics.AUC(name='prc', curve='PR'), # precision-recall curve\n",
    "]\n",
    "\n",
    "def make_model(metrics=METRICS, output_bias=None):\n",
    "  if output_bias is not None:\n",
    "    output_bias = tf.keras.initializers.Constant(output_bias)\n",
    "  model = keras.Sequential([\n",
    "      keras.layers.Dense(\n",
    "          16, activation='relu',\n",
    "          input_shape=(train_features.shape[-1],)),\n",
    "      keras.layers.Dropout(0.5),\n",
    "      keras.layers.Dense(1, activation='sigmoid',\n",
    "                         bias_initializer=output_bias),\n",
    "  ])\n",
    "\n",
    "  model.compile(\n",
    "      optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "      loss=keras.losses.BinaryCrossentropy(),\n",
    "      metrics=metrics)\n",
    "\n",
    "  return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SU0GX6E6mieP"
   },
   "source": [
    "### Understanding useful metrics\n",
    "\n",
    "Notice that there are a few metrics defined above that can be computed by the model that will be helpful when evaluating the performance.\n",
    "\n",
    "\n",
    "\n",
    "*   **False** negatives and **false** positives are samples that were **incorrectly** classified\n",
    "*   **True** negatives and **true** positives are samples that were **correctly** classified\n",
    "*   **Accuracy** is the percentage of examples correctly classified\n",
    ">   $\\frac{\\text{true samples}}{\\text{total samples}}$\n",
    "*   **Precision** is the percentage of **predicted** positives that were correctly classified\n",
    ">   $\\frac{\\text{true positives}}{\\text{true positives + false positives}}$\n",
    "*   **Recall** is the percentage of **actual** positives that were correctly classified\n",
    ">   $\\frac{\\text{true positives}}{\\text{true positives + false negatives}}$\n",
    "*   **AUC** refers to the Area Under the Curve of a Receiver Operating Characteristic curve (ROC-AUC). This metric is equal to the probability that a classifier will rank a random positive sample higher than a random negative sample.\n",
    "*   **AUPRC** refers to Area Under the Curve of the Precision-Recall Curve. This metric computes precision-recall pairs for different probability thresholds. \n",
    "\n",
    "Note: Accuracy is not a helpful metric for this task. You can 99.8%+ accuracy on this task by predicting False all the time.  \n",
    "\n",
    "Read more:\n",
    "*  [True vs. False and Positive vs. Negative](https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative)\n",
    "*  [Accuracy](https://developers.google.com/machine-learning/crash-course/classification/accuracy)\n",
    "*   [Precision and Recall](https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)\n",
    "*   [ROC-AUC](https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc)\n",
    "*   [Relationship between Precision-Recall and ROC Curves](https://www.biostat.wisc.edu/~page/rocpr.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FYdhSAoaF_TK"
   },
   "source": [
    "## Baseline model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IDbltVPg2m2q"
   },
   "source": [
    "### Build the model\n",
    "\n",
    "Now create and train your model using the function that was defined earlier. Notice that the model is fit using a larger than default batch size of 2048, this is important to ensure that each batch has a decent chance of containing a few positive samples. If the batch size was too small, they would likely have no fraudulent transactions to learn from.\n",
    "\n",
    "\n",
    "Note: this model will not handle the class imbalance well. You will improve it later in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "ouUkwPcGQsy3"
   },
   "outputs": [],
   "source": [
    "EPOCHS = 100\n",
    "BATCH_SIZE = 2048\n",
    "\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
    "    monitor='val_prc', \n",
    "    verbose=1,\n",
    "    patience=10,\n",
    "    mode='max',\n",
    "    restore_best_weights=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "1xlR_dekzw7C"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 16)                480       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 497\n",
      "Trainable params: 497\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# TODO 2\n",
    "# Create and train the model by calling the `make_model()` function.\n",
    "model = make_model()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wx7ND3_SqckO"
   },
   "source": [
    "Test run the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "LopSd-yQqO3a"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-11-08 16:12:42.441453: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.14240396],\n",
       "       [0.10327184],\n",
       "       [0.07487038],\n",
       "       [0.33184433],\n",
       "       [0.24053097],\n",
       "       [0.00126556],\n",
       "       [0.09173292],\n",
       "       [0.1832895 ],\n",
       "       [0.21091971],\n",
       "       [0.17918304]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YKIgWqHms_03"
   },
   "source": [
    "### Optional: Set the correct initial bias."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qk_3Ry6EoYDq"
   },
   "source": [
    "These initial guesses are not great. You know the dataset is imbalanced. Set the output layer's bias to reflect that (See: [A Recipe for Training Neural Networks: \"init well\"](http://karpathy.github.io/2019/04/25/recipe/#2-set-up-the-end-to-end-trainingevaluation-skeleton--get-dumb-baselines)). This can help with initial convergence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PdbfWDuVpo6k"
   },
   "source": [
    "With the default bias initialization the loss should be about `math.log(2) = 0.69314` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "H-oPqh3SoGXk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.2348\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
    "print(\"Loss: {:0.4f}\".format(results[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hE-JRzfKqfhB"
   },
   "source": [
    "The correct bias to set can be derived from:\n",
    "\n",
    "$$ p_0 = pos/(pos + neg) = 1/(1+e^{-b_0}) $$\n",
    "$$ b_0 = -log_e(1/p_0 - 1) $$\n",
    "$$ b_0 = log_e(pos/neg)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "F5KWPSjjstUS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-6.35935934])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_bias = np.log([pos/neg])\n",
    "initial_bias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d1juXI9yY1KD"
   },
   "source": [
    "Set that as the initial bias, and the model will give much more reasonable initial guesses. \n",
    "\n",
    "It should be near: `pos/total = 0.0018`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "50oyu1uss0i-"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.69734383e-04],\n",
       "       [6.68376684e-04],\n",
       "       [7.12066889e-04],\n",
       "       [2.43455172e-04],\n",
       "       [1.15574017e-04],\n",
       "       [2.66068018e-05],\n",
       "       [1.19151446e-04],\n",
       "       [2.75284052e-04],\n",
       "       [2.58475542e-04],\n",
       "       [3.60012054e-04]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = make_model(output_bias=initial_bias)\n",
    "model.predict(train_features[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4xqFYb2KqRHQ"
   },
   "source": [
    "With this initialization the initial loss should be approximately:\n",
    "\n",
    "$$-p_0log(p_0)-(1-p_0)log(1-p_0) = 0.01317$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "xVDqCWXDqHSc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss: 0.0192\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(train_features, train_labels, batch_size=BATCH_SIZE, verbose=0)\n",
    "print(\"Loss: {:0.4f}\".format(results[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FrDC8hvNr9yw"
   },
   "source": [
    "This initial loss is about 50 times less than if would have been with naive initialization.\n",
    "\n",
    "This way the model doesn't need to spend the first few epochs just learning that positive examples are unlikely. This also makes it easier to read plots of the loss during training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0EJj9ixKVBMT"
   },
   "source": [
    "### Checkpoint the initial weights\n",
    "\n",
    "To make the various training runs more comparable, keep this initial model's weights in a checkpoint file, and load them into each model before training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "_tSUm4yAVIif"
   },
   "outputs": [],
   "source": [
    "initial_weights = os.path.join(tempfile.mkdtemp(), 'initial_weights')\n",
    "model.save_weights(initial_weights)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EVXiLyqyZ8AX"
   },
   "source": [
    "### Confirm that the bias fix helps\n",
    "\n",
    "Before moving on, confirm quick that the careful bias initialization actually helped.\n",
    "\n",
    "Train the model for 20 epochs, with and without this careful initialization, and compare the losses: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "Dm4-4K5RZ63Q"
   },
   "outputs": [],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "model.layers[-1].bias.assign([0.0])\n",
    "zero_bias_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=20,\n",
    "    validation_data=(val_features, val_labels), \n",
    "    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "j8DsLXHQaSql"
   },
   "outputs": [],
   "source": [
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "careful_bias_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=20,\n",
    "    validation_data=(val_features, val_labels), \n",
    "    verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "E3XsMBjhauFV"
   },
   "outputs": [],
   "source": [
    "def plot_loss(history, label, n):\n",
    "  # Use a log scale on y-axis to show the wide range of values.\n",
    "  plt.semilogy(history.epoch, history.history['loss'],\n",
    "               color=colors[n], label='Train ' + label)\n",
    "  plt.semilogy(history.epoch, history.history['val_loss'],\n",
    "               color=colors[n], label='Val ' + label,\n",
    "               linestyle=\"--\")\n",
    "  plt.xlabel('Epoch')\n",
    "  plt.ylabel('Loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "dxFaskm7beC7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss(zero_bias_history, \"Zero Bias\", 0)\n",
    "plot_loss(careful_bias_history, \"Careful Bias\", 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fKMioV0ddG3R"
   },
   "source": [
    "The above figure makes it clear: In terms of validation loss, on this problem, this careful initialization gives a clear advantage. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RsA_7SEntRaV"
   },
   "source": [
    "### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "yZKAc8NCDnoR"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "90/90 [==============================] - 4s 27ms/step - loss: 0.0140 - tp: 78.0000 - fp: 18.0000 - tn: 227427.0000 - fn: 322.0000 - accuracy: 0.9985 - precision: 0.8125 - recall: 0.1950 - auc: 0.7055 - prc: 0.3028 - val_loss: 0.0068 - val_tp: 1.0000 - val_fp: 0.0000e+00 - val_tn: 45490.0000 - val_fn: 78.0000 - val_accuracy: 0.9983 - val_precision: 1.0000 - val_recall: 0.0127 - val_auc: 0.8859 - val_prc: 0.6578\n",
      "Epoch 2/100\n",
      "90/90 [==============================] - 2s 18ms/step - loss: 0.0085 - tp: 83.0000 - fp: 21.0000 - tn: 181934.0000 - fn: 238.0000 - accuracy: 0.9986 - precision: 0.7981 - recall: 0.2586 - auc: 0.7799 - prc: 0.4207 - val_loss: 0.0046 - val_tp: 41.0000 - val_fp: 8.0000 - val_tn: 45482.0000 - val_fn: 38.0000 - val_accuracy: 0.9990 - val_precision: 0.8367 - val_recall: 0.5190 - val_auc: 0.9176 - val_prc: 0.7035\n",
      "Epoch 3/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0075 - tp: 127.0000 - fp: 28.0000 - tn: 181927.0000 - fn: 194.0000 - accuracy: 0.9988 - precision: 0.8194 - recall: 0.3956 - auc: 0.8059 - prc: 0.4960 - val_loss: 0.0042 - val_tp: 47.0000 - val_fp: 8.0000 - val_tn: 45482.0000 - val_fn: 32.0000 - val_accuracy: 0.9991 - val_precision: 0.8545 - val_recall: 0.5949 - val_auc: 0.9176 - val_prc: 0.7139\n",
      "Epoch 4/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.0066 - tp: 139.0000 - fp: 26.0000 - tn: 181929.0000 - fn: 182.0000 - accuracy: 0.9989 - precision: 0.8424 - recall: 0.4330 - auc: 0.8190 - prc: 0.5498 - val_loss: 0.0040 - val_tp: 49.0000 - val_fp: 8.0000 - val_tn: 45482.0000 - val_fn: 30.0000 - val_accuracy: 0.9992 - val_precision: 0.8596 - val_recall: 0.6203 - val_auc: 0.9176 - val_prc: 0.7188\n",
      "Epoch 5/100\n",
      "90/90 [==============================] - 2s 25ms/step - loss: 0.0068 - tp: 132.0000 - fp: 25.0000 - tn: 181930.0000 - fn: 189.0000 - accuracy: 0.9988 - precision: 0.8408 - recall: 0.4112 - auc: 0.7973 - prc: 0.5070 - val_loss: 0.0039 - val_tp: 50.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 29.0000 - val_accuracy: 0.9991 - val_precision: 0.8333 - val_recall: 0.6329 - val_auc: 0.9239 - val_prc: 0.7343\n",
      "Epoch 6/100\n",
      "90/90 [==============================] - 2s 27ms/step - loss: 0.0066 - tp: 139.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 182.0000 - accuracy: 0.9989 - precision: 0.8373 - recall: 0.4330 - auc: 0.8037 - prc: 0.5145 - val_loss: 0.0038 - val_tp: 50.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 29.0000 - val_accuracy: 0.9992 - val_precision: 0.8475 - val_recall: 0.6329 - val_auc: 0.9239 - val_prc: 0.7475\n",
      "Epoch 7/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 0.0065 - tp: 144.0000 - fp: 32.0000 - tn: 181923.0000 - fn: 177.0000 - accuracy: 0.9989 - precision: 0.8182 - recall: 0.4486 - auc: 0.8254 - prc: 0.5020 - val_loss: 0.0037 - val_tp: 51.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 28.0000 - val_accuracy: 0.9992 - val_precision: 0.8500 - val_recall: 0.6456 - val_auc: 0.9238 - val_prc: 0.7705\n",
      "Epoch 8/100\n",
      "90/90 [==============================] - 1s 11ms/step - loss: 0.0056 - tp: 152.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 169.0000 - accuracy: 0.9989 - precision: 0.8492 - recall: 0.4735 - auc: 0.8724 - prc: 0.5949 - val_loss: 0.0036 - val_tp: 56.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 23.0000 - val_accuracy: 0.9993 - val_precision: 0.8485 - val_recall: 0.7089 - val_auc: 0.9237 - val_prc: 0.7733\n",
      "Epoch 9/100\n",
      "90/90 [==============================] - 1s 9ms/step - loss: 0.0056 - tp: 152.0000 - fp: 29.0000 - tn: 181926.0000 - fn: 169.0000 - accuracy: 0.9989 - precision: 0.8398 - recall: 0.4735 - auc: 0.8783 - prc: 0.5876 - val_loss: 0.0035 - val_tp: 56.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 23.0000 - val_accuracy: 0.9993 - val_precision: 0.8485 - val_recall: 0.7089 - val_auc: 0.9237 - val_prc: 0.7782\n",
      "Epoch 10/100\n",
      "90/90 [==============================] - 1s 12ms/step - loss: 0.0052 - tp: 163.0000 - fp: 29.0000 - tn: 181926.0000 - fn: 158.0000 - accuracy: 0.9990 - precision: 0.8490 - recall: 0.5078 - auc: 0.8926 - prc: 0.6203 - val_loss: 0.0035 - val_tp: 53.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 26.0000 - val_accuracy: 0.9992 - val_precision: 0.8413 - val_recall: 0.6709 - val_auc: 0.9237 - val_prc: 0.7832\n",
      "Epoch 11/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 0.0050 - tp: 158.0000 - fp: 24.0000 - tn: 181931.0000 - fn: 163.0000 - accuracy: 0.9990 - precision: 0.8681 - recall: 0.4922 - auc: 0.8958 - prc: 0.6511 - val_loss: 0.0034 - val_tp: 53.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 26.0000 - val_accuracy: 0.9992 - val_precision: 0.8413 - val_recall: 0.6709 - val_auc: 0.9300 - val_prc: 0.7926\n",
      "Epoch 12/100\n",
      "90/90 [==============================] - 2s 18ms/step - loss: 0.0050 - tp: 162.0000 - fp: 24.0000 - tn: 181931.0000 - fn: 159.0000 - accuracy: 0.9990 - precision: 0.8710 - recall: 0.5047 - auc: 0.8911 - prc: 0.6421 - val_loss: 0.0034 - val_tp: 54.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 25.0000 - val_accuracy: 0.9992 - val_precision: 0.8438 - val_recall: 0.6835 - val_auc: 0.9301 - val_prc: 0.7929\n",
      "Epoch 13/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.0052 - tp: 145.0000 - fp: 23.0000 - tn: 181932.0000 - fn: 176.0000 - accuracy: 0.9989 - precision: 0.8631 - recall: 0.4517 - auc: 0.8769 - prc: 0.6151 - val_loss: 0.0034 - val_tp: 57.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 22.0000 - val_accuracy: 0.9993 - val_precision: 0.8382 - val_recall: 0.7215 - val_auc: 0.9300 - val_prc: 0.7939\n",
      "Epoch 14/100\n",
      "90/90 [==============================] - 2s 23ms/step - loss: 0.0053 - tp: 140.0000 - fp: 31.0000 - tn: 181924.0000 - fn: 181.0000 - accuracy: 0.9988 - precision: 0.8187 - recall: 0.4361 - auc: 0.8787 - prc: 0.6039 - val_loss: 0.0034 - val_tp: 57.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 22.0000 - val_accuracy: 0.9993 - val_precision: 0.8382 - val_recall: 0.7215 - val_auc: 0.9300 - val_prc: 0.7943\n",
      "Epoch 15/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.0050 - tp: 149.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 172.0000 - accuracy: 0.9989 - precision: 0.8466 - recall: 0.4642 - auc: 0.8850 - prc: 0.6396 - val_loss: 0.0033 - val_tp: 58.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 21.0000 - val_accuracy: 0.9993 - val_precision: 0.8406 - val_recall: 0.7342 - val_auc: 0.9300 - val_prc: 0.7953\n",
      "Epoch 16/100\n",
      "90/90 [==============================] - 1s 16ms/step - loss: 0.0050 - tp: 167.0000 - fp: 23.0000 - tn: 181932.0000 - fn: 154.0000 - accuracy: 0.9990 - precision: 0.8789 - recall: 0.5202 - auc: 0.8770 - prc: 0.6319 - val_loss: 0.0033 - val_tp: 59.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 20.0000 - val_accuracy: 0.9993 - val_precision: 0.8429 - val_recall: 0.7468 - val_auc: 0.9299 - val_prc: 0.7959\n",
      "Epoch 17/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 0.0047 - tp: 170.0000 - fp: 28.0000 - tn: 181927.0000 - fn: 151.0000 - accuracy: 0.9990 - precision: 0.8586 - recall: 0.5296 - auc: 0.8881 - prc: 0.6466 - val_loss: 0.0033 - val_tp: 61.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8472 - val_recall: 0.7722 - val_auc: 0.9299 - val_prc: 0.7925\n",
      "Epoch 18/100\n",
      "90/90 [==============================] - 1s 12ms/step - loss: 0.0050 - tp: 163.0000 - fp: 26.0000 - tn: 181929.0000 - fn: 158.0000 - accuracy: 0.9990 - precision: 0.8624 - recall: 0.5078 - auc: 0.8849 - prc: 0.6246 - val_loss: 0.0033 - val_tp: 60.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 19.0000 - val_accuracy: 0.9993 - val_precision: 0.8451 - val_recall: 0.7595 - val_auc: 0.9300 - val_prc: 0.7963\n",
      "Epoch 19/100\n",
      "90/90 [==============================] - 1s 11ms/step - loss: 0.0049 - tp: 161.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 160.0000 - accuracy: 0.9990 - precision: 0.8564 - recall: 0.5016 - auc: 0.8912 - prc: 0.6515 - val_loss: 0.0034 - val_tp: 60.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 19.0000 - val_accuracy: 0.9993 - val_precision: 0.8451 - val_recall: 0.7595 - val_auc: 0.9300 - val_prc: 0.7956\n",
      "Epoch 20/100\n",
      "90/90 [==============================] - 1s 9ms/step - loss: 0.0048 - tp: 161.0000 - fp: 28.0000 - tn: 181927.0000 - fn: 160.0000 - accuracy: 0.9990 - precision: 0.8519 - recall: 0.5016 - auc: 0.8926 - prc: 0.6519 - val_loss: 0.0033 - val_tp: 59.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 20.0000 - val_accuracy: 0.9993 - val_precision: 0.8429 - val_recall: 0.7468 - val_auc: 0.9300 - val_prc: 0.7978\n",
      "Epoch 21/100\n",
      "90/90 [==============================] - 1s 12ms/step - loss: 0.0050 - tp: 163.0000 - fp: 24.0000 - tn: 181931.0000 - fn: 158.0000 - accuracy: 0.9990 - precision: 0.8717 - recall: 0.5078 - auc: 0.8755 - prc: 0.6257 - val_loss: 0.0033 - val_tp: 61.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8472 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.7989\n",
      "Epoch 22/100\n",
      "90/90 [==============================] - 1s 13ms/step - loss: 0.0047 - tp: 164.0000 - fp: 23.0000 - tn: 181932.0000 - fn: 157.0000 - accuracy: 0.9990 - precision: 0.8770 - recall: 0.5109 - auc: 0.8960 - prc: 0.6530 - val_loss: 0.0033 - val_tp: 60.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 19.0000 - val_accuracy: 0.9993 - val_precision: 0.8451 - val_recall: 0.7595 - val_auc: 0.9300 - val_prc: 0.8001\n",
      "Epoch 23/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 0.0046 - tp: 170.0000 - fp: 22.0000 - tn: 181933.0000 - fn: 151.0000 - accuracy: 0.9991 - precision: 0.8854 - recall: 0.5296 - auc: 0.8958 - prc: 0.6574 - val_loss: 0.0033 - val_tp: 61.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8472 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.8010\n",
      "Epoch 24/100\n",
      "90/90 [==============================] - 1s 14ms/step - loss: 0.0047 - tp: 172.0000 - fp: 23.0000 - tn: 181932.0000 - fn: 149.0000 - accuracy: 0.9991 - precision: 0.8821 - recall: 0.5358 - auc: 0.8974 - prc: 0.6538 - val_loss: 0.0033 - val_tp: 62.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 17.0000 - val_accuracy: 0.9994 - val_precision: 0.8493 - val_recall: 0.7848 - val_auc: 0.9300 - val_prc: 0.8001\n",
      "Epoch 25/100\n",
      "90/90 [==============================] - 1s 14ms/step - loss: 0.0046 - tp: 164.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 157.0000 - accuracy: 0.9990 - precision: 0.8454 - recall: 0.5109 - auc: 0.8990 - prc: 0.6570 - val_loss: 0.0033 - val_tp: 61.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8472 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.8013\n",
      "Epoch 26/100\n",
      "90/90 [==============================] - 2s 23ms/step - loss: 0.0048 - tp: 165.0000 - fp: 34.0000 - tn: 181921.0000 - fn: 156.0000 - accuracy: 0.9990 - precision: 0.8291 - recall: 0.5140 - auc: 0.8849 - prc: 0.6404 - val_loss: 0.0033 - val_tp: 61.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8472 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.8008\n",
      "Epoch 27/100\n",
      "90/90 [==============================] - 2s 24ms/step - loss: 0.0047 - tp: 159.0000 - fp: 32.0000 - tn: 181923.0000 - fn: 162.0000 - accuracy: 0.9989 - precision: 0.8325 - recall: 0.4953 - auc: 0.8928 - prc: 0.6494 - val_loss: 0.0033 - val_tp: 60.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 19.0000 - val_accuracy: 0.9993 - val_precision: 0.8451 - val_recall: 0.7595 - val_auc: 0.9300 - val_prc: 0.8017\n",
      "Epoch 28/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0048 - tp: 158.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 163.0000 - accuracy: 0.9989 - precision: 0.8404 - recall: 0.4922 - auc: 0.8772 - prc: 0.6375 - val_loss: 0.0034 - val_tp: 60.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 19.0000 - val_accuracy: 0.9994 - val_precision: 0.8696 - val_recall: 0.7595 - val_auc: 0.9300 - val_prc: 0.8048\n",
      "Epoch 29/100\n",
      "90/90 [==============================] - 1s 15ms/step - loss: 0.0048 - tp: 152.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 169.0000 - accuracy: 0.9989 - precision: 0.8492 - recall: 0.4735 - auc: 0.8803 - prc: 0.6495 - val_loss: 0.0033 - val_tp: 61.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8472 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.8028\n",
      "Epoch 30/100\n",
      "90/90 [==============================] - 1s 9ms/step - loss: 0.0044 - tp: 176.0000 - fp: 29.0000 - tn: 181926.0000 - fn: 145.0000 - accuracy: 0.9990 - precision: 0.8585 - recall: 0.5483 - auc: 0.8944 - prc: 0.6751 - val_loss: 0.0034 - val_tp: 62.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 17.0000 - val_accuracy: 0.9994 - val_precision: 0.8493 - val_recall: 0.7848 - val_auc: 0.9300 - val_prc: 0.8019\n",
      "Epoch 31/100\n",
      "90/90 [==============================] - 2s 17ms/step - loss: 0.0051 - tp: 140.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 181.0000 - accuracy: 0.9988 - precision: 0.8235 - recall: 0.4361 - auc: 0.8740 - prc: 0.6125 - val_loss: 0.0034 - val_tp: 65.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8553 - val_recall: 0.8228 - val_auc: 0.9300 - val_prc: 0.8024\n",
      "Epoch 32/100\n",
      "90/90 [==============================] - 2s 22ms/step - loss: 0.0044 - tp: 180.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 141.0000 - accuracy: 0.9991 - precision: 0.8696 - recall: 0.5607 - auc: 0.8944 - prc: 0.6684 - val_loss: 0.0034 - val_tp: 62.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 17.0000 - val_accuracy: 0.9994 - val_precision: 0.8611 - val_recall: 0.7848 - val_auc: 0.9300 - val_prc: 0.8042\n",
      "Epoch 33/100\n",
      "90/90 [==============================] - 2s 25ms/step - loss: 0.0044 - tp: 169.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 152.0000 - accuracy: 0.9990 - precision: 0.8622 - recall: 0.5265 - auc: 0.8960 - prc: 0.6752 - val_loss: 0.0034 - val_tp: 61.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8472 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.8029\n",
      "Epoch 34/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0043 - tp: 168.0000 - fp: 20.0000 - tn: 181935.0000 - fn: 153.0000 - accuracy: 0.9991 - precision: 0.8936 - recall: 0.5234 - auc: 0.8975 - prc: 0.6852 - val_loss: 0.0034 - val_tp: 66.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 13.0000 - val_accuracy: 0.9995 - val_precision: 0.8571 - val_recall: 0.8354 - val_auc: 0.9300 - val_prc: 0.8052\n",
      "Epoch 35/100\n",
      "90/90 [==============================] - 1s 13ms/step - loss: 0.0046 - tp: 174.0000 - fp: 39.0000 - tn: 181916.0000 - fn: 147.0000 - accuracy: 0.9990 - precision: 0.8169 - recall: 0.5421 - auc: 0.8991 - prc: 0.6405 - val_loss: 0.0034 - val_tp: 63.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 16.0000 - val_accuracy: 0.9994 - val_precision: 0.8630 - val_recall: 0.7975 - val_auc: 0.9301 - val_prc: 0.8094\n",
      "Epoch 36/100\n",
      "90/90 [==============================] - 2s 22ms/step - loss: 0.0045 - tp: 168.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 153.0000 - accuracy: 0.9990 - precision: 0.8615 - recall: 0.5234 - auc: 0.8929 - prc: 0.6654 - val_loss: 0.0034 - val_tp: 63.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 16.0000 - val_accuracy: 0.9994 - val_precision: 0.8630 - val_recall: 0.7975 - val_auc: 0.9300 - val_prc: 0.8103\n",
      "Epoch 37/100\n",
      "90/90 [==============================] - 2s 22ms/step - loss: 0.0045 - tp: 171.0000 - fp: 31.0000 - tn: 181924.0000 - fn: 150.0000 - accuracy: 0.9990 - precision: 0.8465 - recall: 0.5327 - auc: 0.8928 - prc: 0.6591 - val_loss: 0.0035 - val_tp: 64.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.8649 - val_recall: 0.8101 - val_auc: 0.9300 - val_prc: 0.8086\n",
      "Epoch 38/100\n",
      "90/90 [==============================] - 1s 15ms/step - loss: 0.0045 - tp: 172.0000 - fp: 32.0000 - tn: 181923.0000 - fn: 149.0000 - accuracy: 0.9990 - precision: 0.8431 - recall: 0.5358 - auc: 0.8960 - prc: 0.6610 - val_loss: 0.0035 - val_tp: 57.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 22.0000 - val_accuracy: 0.9993 - val_precision: 0.8636 - val_recall: 0.7215 - val_auc: 0.9300 - val_prc: 0.8116\n",
      "Epoch 39/100\n",
      "90/90 [==============================] - 2s 24ms/step - loss: 0.0048 - tp: 164.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 157.0000 - accuracy: 0.9990 - precision: 0.8454 - recall: 0.5109 - auc: 0.8849 - prc: 0.6278 - val_loss: 0.0035 - val_tp: 63.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 16.0000 - val_accuracy: 0.9994 - val_precision: 0.8630 - val_recall: 0.7975 - val_auc: 0.9300 - val_prc: 0.8104\n",
      "Epoch 40/100\n",
      "90/90 [==============================] - 2s 24ms/step - loss: 0.0047 - tp: 162.0000 - fp: 34.0000 - tn: 181921.0000 - fn: 159.0000 - accuracy: 0.9989 - precision: 0.8265 - recall: 0.5047 - auc: 0.8928 - prc: 0.6401 - val_loss: 0.0035 - val_tp: 55.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 24.0000 - val_accuracy: 0.9993 - val_precision: 0.8594 - val_recall: 0.6962 - val_auc: 0.9300 - val_prc: 0.8131\n",
      "Epoch 41/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0042 - tp: 178.0000 - fp: 19.0000 - tn: 181936.0000 - fn: 143.0000 - accuracy: 0.9991 - precision: 0.9036 - recall: 0.5545 - auc: 0.8928 - prc: 0.6852 - val_loss: 0.0035 - val_tp: 64.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.8649 - val_recall: 0.8101 - val_auc: 0.9300 - val_prc: 0.8096\n",
      "Epoch 42/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0043 - tp: 180.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 141.0000 - accuracy: 0.9991 - precision: 0.8571 - recall: 0.5607 - auc: 0.9037 - prc: 0.6859 - val_loss: 0.0035 - val_tp: 66.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 13.0000 - val_accuracy: 0.9995 - val_precision: 0.8571 - val_recall: 0.8354 - val_auc: 0.9300 - val_prc: 0.8054\n",
      "Epoch 43/100\n",
      "90/90 [==============================] - 2s 24ms/step - loss: 0.0045 - tp: 173.0000 - fp: 31.0000 - tn: 181924.0000 - fn: 148.0000 - accuracy: 0.9990 - precision: 0.8480 - recall: 0.5389 - auc: 0.8943 - prc: 0.6457 - val_loss: 0.0035 - val_tp: 65.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8553 - val_recall: 0.8228 - val_auc: 0.9300 - val_prc: 0.8078\n",
      "Epoch 44/100\n",
      "90/90 [==============================] - 2s 24ms/step - loss: 0.0045 - tp: 166.0000 - fp: 32.0000 - tn: 181923.0000 - fn: 155.0000 - accuracy: 0.9990 - precision: 0.8384 - recall: 0.5171 - auc: 0.8927 - prc: 0.6476 - val_loss: 0.0035 - val_tp: 57.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 22.0000 - val_accuracy: 0.9993 - val_precision: 0.8636 - val_recall: 0.7215 - val_auc: 0.9300 - val_prc: 0.8151\n",
      "Epoch 45/100\n",
      "90/90 [==============================] - 2s 27ms/step - loss: 0.0043 - tp: 167.0000 - fp: 24.0000 - tn: 181931.0000 - fn: 154.0000 - accuracy: 0.9990 - precision: 0.8743 - recall: 0.5202 - auc: 0.8929 - prc: 0.6754 - val_loss: 0.0036 - val_tp: 64.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.8649 - val_recall: 0.8101 - val_auc: 0.9300 - val_prc: 0.8122\n",
      "Epoch 46/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.0044 - tp: 169.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 152.0000 - accuracy: 0.9990 - precision: 0.8492 - recall: 0.5265 - auc: 0.8912 - prc: 0.6558 - val_loss: 0.0036 - val_tp: 65.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8667 - val_recall: 0.8228 - val_auc: 0.9300 - val_prc: 0.8113\n",
      "Epoch 47/100\n",
      "90/90 [==============================] - 2s 20ms/step - loss: 0.0044 - tp: 170.0000 - fp: 34.0000 - tn: 181921.0000 - fn: 151.0000 - accuracy: 0.9990 - precision: 0.8333 - recall: 0.5296 - auc: 0.9038 - prc: 0.6663 - val_loss: 0.0036 - val_tp: 65.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8784 - val_recall: 0.8228 - val_auc: 0.9300 - val_prc: 0.8154\n",
      "Epoch 48/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0040 - tp: 182.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 139.0000 - accuracy: 0.9991 - precision: 0.8708 - recall: 0.5670 - auc: 0.9101 - prc: 0.7000 - val_loss: 0.0036 - val_tp: 65.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8667 - val_recall: 0.8228 - val_auc: 0.9300 - val_prc: 0.8124\n",
      "Epoch 49/100\n",
      "90/90 [==============================] - 2s 19ms/step - loss: 0.0042 - tp: 185.0000 - fp: 26.0000 - tn: 181929.0000 - fn: 136.0000 - accuracy: 0.9991 - precision: 0.8768 - recall: 0.5763 - auc: 0.9007 - prc: 0.6855 - val_loss: 0.0036 - val_tp: 62.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 17.0000 - val_accuracy: 0.9994 - val_precision: 0.8732 - val_recall: 0.7848 - val_auc: 0.9300 - val_prc: 0.8141\n",
      "Epoch 50/100\n",
      "90/90 [==============================] - 2s 25ms/step - loss: 0.0046 - tp: 174.0000 - fp: 26.0000 - tn: 181929.0000 - fn: 147.0000 - accuracy: 0.9991 - precision: 0.8700 - recall: 0.5421 - auc: 0.8834 - prc: 0.6512 - val_loss: 0.0036 - val_tp: 65.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8667 - val_recall: 0.8228 - val_auc: 0.9300 - val_prc: 0.8145\n",
      "Epoch 51/100\n",
      "90/90 [==============================] - 2s 23ms/step - loss: 0.0044 - tp: 176.0000 - fp: 25.0000 - tn: 181930.0000 - fn: 145.0000 - accuracy: 0.9991 - precision: 0.8756 - recall: 0.5483 - auc: 0.8865 - prc: 0.6538 - val_loss: 0.0036 - val_tp: 63.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 16.0000 - val_accuracy: 0.9995 - val_precision: 0.8750 - val_recall: 0.7975 - val_auc: 0.9301 - val_prc: 0.8162\n",
      "Epoch 52/100\n",
      "90/90 [==============================] - 1s 15ms/step - loss: 0.0040 - tp: 179.0000 - fp: 20.0000 - tn: 181935.0000 - fn: 142.0000 - accuracy: 0.9991 - precision: 0.8995 - recall: 0.5576 - auc: 0.9009 - prc: 0.7052 - val_loss: 0.0037 - val_tp: 65.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8667 - val_recall: 0.8228 - val_auc: 0.9301 - val_prc: 0.8125\n",
      "Epoch 53/100\n",
      "90/90 [==============================] - 1s 8ms/step - loss: 0.0040 - tp: 184.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 137.0000 - accuracy: 0.9991 - precision: 0.8598 - recall: 0.5732 - auc: 0.9180 - prc: 0.6968 - val_loss: 0.0037 - val_tp: 64.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.8649 - val_recall: 0.8101 - val_auc: 0.9300 - val_prc: 0.8135\n",
      "Epoch 54/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 0.0044 - tp: 178.0000 - fp: 28.0000 - tn: 181927.0000 - fn: 143.0000 - accuracy: 0.9991 - precision: 0.8641 - recall: 0.5545 - auc: 0.8975 - prc: 0.6556 - val_loss: 0.0037 - val_tp: 58.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 21.0000 - val_accuracy: 0.9993 - val_precision: 0.8657 - val_recall: 0.7342 - val_auc: 0.9300 - val_prc: 0.8135\n",
      "Epoch 55/100\n",
      "90/90 [==============================] - 2s 19ms/step - loss: 0.0043 - tp: 168.0000 - fp: 24.0000 - tn: 181931.0000 - fn: 153.0000 - accuracy: 0.9990 - precision: 0.8750 - recall: 0.5234 - auc: 0.8975 - prc: 0.6641 - val_loss: 0.0037 - val_tp: 64.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.8649 - val_recall: 0.8101 - val_auc: 0.9300 - val_prc: 0.8126\n",
      "Epoch 56/100\n",
      "90/90 [==============================] - 1s 15ms/step - loss: 0.0042 - tp: 177.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 144.0000 - accuracy: 0.9991 - precision: 0.8676 - recall: 0.5514 - auc: 0.8959 - prc: 0.6760 - val_loss: 0.0037 - val_tp: 62.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 17.0000 - val_accuracy: 0.9994 - val_precision: 0.8732 - val_recall: 0.7848 - val_auc: 0.9300 - val_prc: 0.8145\n",
      "Epoch 57/100\n",
      "90/90 [==============================] - 1s 13ms/step - loss: 0.0043 - tp: 174.0000 - fp: 24.0000 - tn: 181931.0000 - fn: 147.0000 - accuracy: 0.9991 - precision: 0.8788 - recall: 0.5421 - auc: 0.8912 - prc: 0.6643 - val_loss: 0.0038 - val_tp: 58.0000 - val_fp: 8.0000 - val_tn: 45482.0000 - val_fn: 21.0000 - val_accuracy: 0.9994 - val_precision: 0.8788 - val_recall: 0.7342 - val_auc: 0.9300 - val_prc: 0.8144\n",
      "Epoch 58/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0042 - tp: 173.0000 - fp: 24.0000 - tn: 181931.0000 - fn: 148.0000 - accuracy: 0.9991 - precision: 0.8782 - recall: 0.5389 - auc: 0.8912 - prc: 0.6784 - val_loss: 0.0038 - val_tp: 65.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 14.0000 - val_accuracy: 0.9995 - val_precision: 0.8667 - val_recall: 0.8228 - val_auc: 0.9300 - val_prc: 0.8171\n",
      "Epoch 59/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.0041 - tp: 175.0000 - fp: 29.0000 - tn: 181926.0000 - fn: 146.0000 - accuracy: 0.9990 - precision: 0.8578 - recall: 0.5452 - auc: 0.9083 - prc: 0.6823 - val_loss: 0.0038 - val_tp: 61.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8714 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.8149\n",
      "Epoch 60/100\n",
      "90/90 [==============================] - 2s 27ms/step - loss: 0.0040 - tp: 185.0000 - fp: 27.0000 - tn: 181928.0000 - fn: 136.0000 - accuracy: 0.9991 - precision: 0.8726 - recall: 0.5763 - auc: 0.9037 - prc: 0.6919 - val_loss: 0.0038 - val_tp: 58.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 21.0000 - val_accuracy: 0.9993 - val_precision: 0.8657 - val_recall: 0.7342 - val_auc: 0.9300 - val_prc: 0.8139\n",
      "Epoch 61/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.0040 - tp: 192.0000 - fp: 30.0000 - tn: 181925.0000 - fn: 129.0000 - accuracy: 0.9991 - precision: 0.8649 - recall: 0.5981 - auc: 0.9068 - prc: 0.6917 - val_loss: 0.0038 - val_tp: 58.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 21.0000 - val_accuracy: 0.9993 - val_precision: 0.8657 - val_recall: 0.7342 - val_auc: 0.9301 - val_prc: 0.8166\n",
      "Epoch 62/100\n",
      "90/90 [==============================] - 1s 15ms/step - loss: 0.0040 - tp: 180.0000 - fp: 32.0000 - tn: 181923.0000 - fn: 141.0000 - accuracy: 0.9991 - precision: 0.8491 - recall: 0.5607 - auc: 0.9115 - prc: 0.6903 - val_loss: 0.0039 - val_tp: 58.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 21.0000 - val_accuracy: 0.9993 - val_precision: 0.8657 - val_recall: 0.7342 - val_auc: 0.9300 - val_prc: 0.8157\n",
      "Epoch 63/100\n",
      "90/90 [==============================] - 2s 18ms/step - loss: 0.0041 - tp: 179.0000 - fp: 31.0000 - tn: 181924.0000 - fn: 142.0000 - accuracy: 0.9991 - precision: 0.8524 - recall: 0.5576 - auc: 0.9053 - prc: 0.6856 - val_loss: 0.0039 - val_tp: 58.0000 - val_fp: 8.0000 - val_tn: 45482.0000 - val_fn: 21.0000 - val_accuracy: 0.9994 - val_precision: 0.8788 - val_recall: 0.7342 - val_auc: 0.9300 - val_prc: 0.8158\n",
      "Epoch 64/100\n",
      "90/90 [==============================] - 1s 16ms/step - loss: 0.0039 - tp: 188.0000 - fp: 25.0000 - tn: 181930.0000 - fn: 133.0000 - accuracy: 0.9991 - precision: 0.8826 - recall: 0.5857 - auc: 0.9193 - prc: 0.7122 - val_loss: 0.0039 - val_tp: 59.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 20.0000 - val_accuracy: 0.9994 - val_precision: 0.8676 - val_recall: 0.7468 - val_auc: 0.9300 - val_prc: 0.8149\n",
      "Epoch 65/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0038 - tp: 194.0000 - fp: 25.0000 - tn: 181930.0000 - fn: 127.0000 - accuracy: 0.9992 - precision: 0.8858 - recall: 0.6044 - auc: 0.9100 - prc: 0.7140 - val_loss: 0.0039 - val_tp: 61.0000 - val_fp: 8.0000 - val_tn: 45482.0000 - val_fn: 18.0000 - val_accuracy: 0.9994 - val_precision: 0.8841 - val_recall: 0.7722 - val_auc: 0.9300 - val_prc: 0.8153\n",
      "Epoch 66/100\n",
      "90/90 [==============================] - 2s 21ms/step - loss: 0.0038 - tp: 182.0000 - fp: 28.0000 - tn: 181927.0000 - fn: 139.0000 - accuracy: 0.9991 - precision: 0.8667 - recall: 0.5670 - auc: 0.9147 - prc: 0.7096 - val_loss: 0.0039 - val_tp: 64.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.8649 - val_recall: 0.8101 - val_auc: 0.9300 - val_prc: 0.8162\n",
      "Epoch 67/100\n",
      "90/90 [==============================] - 1s 12ms/step - loss: 0.0038 - tp: 189.0000 - fp: 26.0000 - tn: 181929.0000 - fn: 132.0000 - accuracy: 0.9991 - precision: 0.8791 - recall: 0.5888 - auc: 0.9147 - prc: 0.7104 - val_loss: 0.0040 - val_tp: 58.0000 - val_fp: 7.0000 - val_tn: 45483.0000 - val_fn: 21.0000 - val_accuracy: 0.9994 - val_precision: 0.8923 - val_recall: 0.7342 - val_auc: 0.9300 - val_prc: 0.8158\n",
      "Epoch 68/100\n",
      "90/90 [==============================] - 1s 11ms/step - loss: 0.0041 - tp: 178.0000 - fp: 23.0000 - tn: 181932.0000 - fn: 143.0000 - accuracy: 0.9991 - precision: 0.8856 - recall: 0.5545 - auc: 0.8957 - prc: 0.6750 - val_loss: 0.0040 - val_tp: 64.0000 - val_fp: 9.0000 - val_tn: 45481.0000 - val_fn: 15.0000 - val_accuracy: 0.9995 - val_precision: 0.8767 - val_recall: 0.8101 - val_auc: 0.9300 - val_prc: 0.8158\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00068: early stopping\n"
     ]
    }
   ],
   "source": [
    "# TODO 3\n",
    "# Train the model.\n",
    "model = make_model()\n",
    "model.load_weights(initial_weights)\n",
    "baseline_history = model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=(val_features, val_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iSaDBYU9xtP6"
   },
   "source": [
    "### Check training history\n",
    "\n",
    "In this section, you will produce plots of your model's accuracy and loss on the training and validation set. These are useful to check for overfitting, which you can learn more about in the [Overfit and underfit](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit) tutorial.\n",
    "\n",
    "Additionally, you can produce these plots for any of the metrics you created above. False negatives are included as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "WTSkhT1jyGu6"
   },
   "outputs": [],
   "source": [
    "def plot_metrics(history):\n",
    "  metrics = ['loss', 'prc', 'precision', 'recall']\n",
    "  for n, metric in enumerate(metrics):\n",
    "    name = metric.replace(\"_\",\" \").capitalize()\n",
    "    plt.subplot(2,2,n+1)\n",
    "    plt.plot(history.epoch, history.history[metric], color=colors[0], label='Train')\n",
    "    plt.plot(history.epoch, history.history['val_'+metric],\n",
    "             color=colors[0], linestyle=\"--\", label='Val')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel(name)\n",
    "    if metric == 'loss':\n",
    "      plt.ylim([0, plt.ylim()[1]])\n",
    "    elif metric == 'auc':\n",
    "      plt.ylim([0.8,1])\n",
    "    else:\n",
    "      plt.ylim([0,1])\n",
    "\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "u6LReDsqlZlk"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(baseline_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UCa4iWo6WDKR"
   },
   "source": [
    "Note: That the validation curve generally performs better than the training curve. This is mainly caused by the fact that the dropout layer is not active when evaluating the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aJC1booryouo"
   },
   "source": [
    "### Evaluate metrics\n",
    "\n",
    "You can use a [confusion matrix](https://developers.google.com/machine-learning/glossary/#confusion_matrix) to summarize the actual vs. predicted labels, where the X axis is the predicted label and the Y axis is the actual label:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "aNS796IJKrev"
   },
   "outputs": [],
   "source": [
    "train_predictions_baseline = model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_baseline = model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "MVWBGfADwbWI"
   },
   "outputs": [],
   "source": [
    "def plot_cm(labels, predictions, p=0.5):\n",
    "  cm = confusion_matrix(labels, predictions > p)\n",
    "  plt.figure(figsize=(5,5))\n",
    "  sns.heatmap(cm, annot=True, fmt=\"d\")\n",
    "  plt.title('Confusion matrix @{:.2f}'.format(p))\n",
    "  plt.ylabel('Actual label')\n",
    "  plt.xlabel('Predicted label')\n",
    "\n",
    "  print('Legitimate Transactions Detected (True Negatives): ', cm[0][0])\n",
    "  print('Legitimate Transactions Incorrectly Detected (False Positives): ', cm[0][1])\n",
    "  print('Fraudulent Transactions Missed (False Negatives): ', cm[1][0])\n",
    "  print('Fraudulent Transactions Detected (True Positives): ', cm[1][1])\n",
    "  print('Total Fraudulent Transactions: ', np.sum(cm[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nOTjD5Z5Wp1U"
   },
   "source": [
    "Evaluate your model on the test dataset and display the results for the metrics you created above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "poh_hZngt2_9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.003545712446793914\n",
      "tp :  67.0\n",
      "fp :  3.0\n",
      "tn :  56867.0\n",
      "fn :  25.0\n",
      "accuracy :  0.9995084404945374\n",
      "precision :  0.9571428298950195\n",
      "recall :  0.72826087474823\n",
      "auc :  0.9126077890396118\n",
      "prc :  0.7893762588500977\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  56867\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  3\n",
      "Fraudulent Transactions Missed (False Negatives):  25\n",
      "Fraudulent Transactions Detected (True Positives):  67\n",
      "Total Fraudulent Transactions:  92\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TODO 4\n",
    "# Evaluate your model on test dataset.\n",
    "baseline_results = model.evaluate(test_features, test_labels,\n",
    "                                  batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(model.metrics_names, baseline_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_baseline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PyZtSr1v6L4t"
   },
   "source": [
    "If the model had predicted everything perfectly, this would be a [diagonal matrix](https://en.wikipedia.org/wiki/Diagonal_matrix) where values off the main diagonal, indicating incorrect predictions, would be zero. In this case the matrix shows that you have relatively few false positives, meaning that there were relatively few legitimate transactions that were incorrectly flagged. However, you would likely want to have even fewer false negatives despite the cost of increasing the number of false positives. This trade off may be preferable because false negatives would allow fraudulent transactions to go through, whereas false positives may cause an email to be sent to a customer to ask them to verify their card activity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P-QpQsip_F2Q"
   },
   "source": [
    "### Plot the ROC\n",
    "\n",
    "Now plot the [ROC](https://developers.google.com/machine-learning/glossary#ROC). This plot is useful because it shows, at a glance, the range of performance the model can reach just by tuning the output threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "lhaxsLSvANF9"
   },
   "outputs": [],
   "source": [
    "def plot_roc(name, labels, predictions, **kwargs):\n",
    "  fp, tp, _ = sklearn.metrics.roc_curve(labels, predictions)\n",
    "\n",
    "  plt.plot(100*fp, 100*tp, label=name, linewidth=2, **kwargs)\n",
    "  plt.xlabel('False positives [%]')\n",
    "  plt.ylabel('True positives [%]')\n",
    "  plt.xlim([-0.5,20])\n",
    "  plt.ylim([80,100.5])\n",
    "  plt.grid(True)\n",
    "  ax = plt.gca()\n",
    "  ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "DfHHspttKJE0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3384e921d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Y5twGRLfNwmO"
   },
   "source": [
    "### Plot the AUPRC\n",
    "\n",
    "Now plot the [AUPRC](https://developers.google.com/machine-learning/glossary?hl=en#PR_AUC). Area under the interpolated precision-recall curve, obtained by plotting (recall, precision) points for different values of the classification threshold. Depending on how it's calculated, PR AUC may be equivalent to the average precision of the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "XV6JSlFGEqGI"
   },
   "outputs": [],
   "source": [
    "def plot_prc(name, labels, predictions, **kwargs):\n",
    "    precision, recall, _ = sklearn.metrics.precision_recall_curve(labels, predictions)\n",
    "\n",
    "    plt.plot(precision, recall, label=name, linewidth=2, **kwargs)\n",
    "    plt.xlabel('Recall')\n",
    "    plt.ylabel('Precision')\n",
    "    plt.grid(True)\n",
    "    ax = plt.gca()\n",
    "    ax.set_aspect('equal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "FdQs_PcqEsiL"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3386f28190>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gpdsFyp64DhY"
   },
   "source": [
    "It looks like the precision is relatively high, but the recall and the area under the ROC curve (AUC) aren't as high as you might like. Classifiers often face challenges when trying to maximize both precision and recall, which is especially true when working with imbalanced datasets. It is important to consider the costs of different types of errors in the context of the problem you care about. In this example, a false negative (a fraudulent transaction is missed) may have a financial cost, while a false positive (a transaction is incorrectly flagged as fraudulent) may decrease user happiness."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cveQoiMyGQCo"
   },
   "source": [
    "## Class weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ePGp6GUE1WfH"
   },
   "source": [
    "### Calculate class weights\n",
    "\n",
    "The goal is to identify fraudulent transactions, but you don't have very many of those positive samples to work with, so you would want to have the classifier heavily weight the few examples that are available. You can do this by passing Keras weights for each class through a parameter. These will cause the model to \"pay more attention\" to examples from an under-represented class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "qjGWErngGny7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Weight for class 0: 0.50\n",
      "Weight for class 1: 289.44\n"
     ]
    }
   ],
   "source": [
    "# Scaling by total/2 helps keep the loss to a similar magnitude.\n",
    "# The sum of the weights of all examples stays the same.\n",
    "weight_for_0 = (1 / neg) * (total / 2.0)\n",
    "weight_for_1 = (1 / pos) * (total / 2.0)\n",
    "\n",
    "class_weight = {0: weight_for_0, 1: weight_for_1}\n",
    "\n",
    "print('Weight for class 0: {:.2f}'.format(weight_for_0))\n",
    "print('Weight for class 1: {:.2f}'.format(weight_for_1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Mk1OOE2ZSHzy"
   },
   "source": [
    "### Train a model with class weights\n",
    "\n",
    "Now try re-training and evaluating the model with class weights to see how that affects the predictions.\n",
    "\n",
    "Note: Using `class_weights` changes the range of the loss. This may affect the stability of the training depending on the optimizer. Optimizers whose step size is dependent on the magnitude of the gradient, like `tf.keras.optimizers.SGD`, may fail. The optimizer used here, `tf.keras.optimizers.Adam`, is unaffected by the scaling change. Also note that because of the weighting, the total losses are not comparable between the two models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "UJ589fn8ST3x"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "90/90 [==============================] - 5s 33ms/step - loss: 3.5073 - tp: 92.0000 - fp: 12.0000 - tn: 238813.0000 - fn: 321.0000 - accuracy: 0.9986 - precision: 0.8846 - recall: 0.2228 - auc: 0.7212 - prc: 0.3214 - val_loss: 0.0066 - val_tp: 6.0000 - val_fp: 0.0000e+00 - val_tn: 45490.0000 - val_fn: 73.0000 - val_accuracy: 0.9984 - val_precision: 1.0000 - val_recall: 0.0759 - val_auc: 0.8859 - val_prc: 0.6552\n",
      "Epoch 2/100\n",
      "90/90 [==============================] - 2s 23ms/step - loss: 1.8524 - tp: 110.0000 - fp: 28.0000 - tn: 181927.0000 - fn: 211.0000 - accuracy: 0.9987 - precision: 0.7971 - recall: 0.3427 - auc: 0.7923 - prc: 0.4250 - val_loss: 0.0046 - val_tp: 50.0000 - val_fp: 10.0000 - val_tn: 45480.0000 - val_fn: 29.0000 - val_accuracy: 0.9991 - val_precision: 0.8333 - val_recall: 0.6329 - val_auc: 0.9175 - val_prc: 0.6834\n",
      "Epoch 3/100\n",
      "90/90 [==============================] - 2s 22ms/step - loss: 1.4330 - tp: 162.0000 - fp: 37.0000 - tn: 181918.0000 - fn: 159.0000 - accuracy: 0.9989 - precision: 0.8141 - recall: 0.5047 - auc: 0.8100 - prc: 0.4705 - val_loss: 0.0048 - val_tp: 55.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 24.0000 - val_accuracy: 0.9992 - val_precision: 0.8333 - val_recall: 0.6962 - val_auc: 0.9234 - val_prc: 0.7032\n",
      "Epoch 4/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 1.2117 - tp: 180.0000 - fp: 42.0000 - tn: 181913.0000 - fn: 141.0000 - accuracy: 0.9990 - precision: 0.8108 - recall: 0.5607 - auc: 0.8527 - prc: 0.4958 - val_loss: 0.0055 - val_tp: 62.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 17.0000 - val_accuracy: 0.9994 - val_precision: 0.8493 - val_recall: 0.7848 - val_auc: 0.9340 - val_prc: 0.7231\n",
      "Epoch 5/100\n",
      "90/90 [==============================] - 1s 8ms/step - loss: 0.9511 - tp: 205.0000 - fp: 57.0000 - tn: 181898.0000 - fn: 116.0000 - accuracy: 0.9991 - precision: 0.7824 - recall: 0.6386 - auc: 0.8815 - prc: 0.5647 - val_loss: 0.0064 - val_tp: 66.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 13.0000 - val_accuracy: 0.9995 - val_precision: 0.8571 - val_recall: 0.8354 - val_auc: 0.9487 - val_prc: 0.7182\n",
      "Epoch 6/100\n",
      "90/90 [==============================] - 2s 19ms/step - loss: 0.8217 - tp: 214.0000 - fp: 90.0000 - tn: 181865.0000 - fn: 107.0000 - accuracy: 0.9989 - precision: 0.7039 - recall: 0.6667 - auc: 0.8822 - prc: 0.5876 - val_loss: 0.0077 - val_tp: 66.0000 - val_fp: 11.0000 - val_tn: 45479.0000 - val_fn: 13.0000 - val_accuracy: 0.9995 - val_precision: 0.8571 - val_recall: 0.8354 - val_auc: 0.9452 - val_prc: 0.7187\n",
      "Epoch 7/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.7590 - tp: 223.0000 - fp: 243.0000 - tn: 181712.0000 - fn: 98.0000 - accuracy: 0.9981 - precision: 0.4785 - recall: 0.6947 - auc: 0.8834 - prc: 0.5889 - val_loss: 0.0093 - val_tp: 66.0000 - val_fp: 16.0000 - val_tn: 45474.0000 - val_fn: 13.0000 - val_accuracy: 0.9994 - val_precision: 0.8049 - val_recall: 0.8354 - val_auc: 0.9489 - val_prc: 0.7190\n",
      "Epoch 8/100\n",
      "90/90 [==============================] - 2s 26ms/step - loss: 0.7586 - tp: 225.0000 - fp: 433.0000 - tn: 181522.0000 - fn: 96.0000 - accuracy: 0.9971 - precision: 0.3419 - recall: 0.7009 - auc: 0.8779 - prc: 0.5368 - val_loss: 0.0117 - val_tp: 67.0000 - val_fp: 25.0000 - val_tn: 45465.0000 - val_fn: 12.0000 - val_accuracy: 0.9992 - val_precision: 0.7283 - val_recall: 0.8481 - val_auc: 0.9453 - val_prc: 0.7203\n",
      "Epoch 9/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 0.6463 - tp: 239.0000 - fp: 816.0000 - tn: 181139.0000 - fn: 82.0000 - accuracy: 0.9951 - precision: 0.2265 - recall: 0.7445 - auc: 0.8891 - prc: 0.5590 - val_loss: 0.0152 - val_tp: 67.0000 - val_fp: 64.0000 - val_tn: 45426.0000 - val_fn: 12.0000 - val_accuracy: 0.9983 - val_precision: 0.5115 - val_recall: 0.8481 - val_auc: 0.9438 - val_prc: 0.7232\n",
      "Epoch 10/100\n",
      "90/90 [==============================] - 2s 20ms/step - loss: 0.6161 - tp: 236.0000 - fp: 1562.0000 - tn: 180393.0000 - fn: 85.0000 - accuracy: 0.9910 - precision: 0.1313 - recall: 0.7352 - auc: 0.9019 - prc: 0.4325 - val_loss: 0.0222 - val_tp: 67.0000 - val_fp: 166.0000 - val_tn: 45324.0000 - val_fn: 12.0000 - val_accuracy: 0.9961 - val_precision: 0.2876 - val_recall: 0.8481 - val_auc: 0.9560 - val_prc: 0.7005\n",
      "Epoch 11/100\n",
      "90/90 [==============================] - 1s 13ms/step - loss: 0.5972 - tp: 240.0000 - fp: 2368.0000 - tn: 179587.0000 - fn: 81.0000 - accuracy: 0.9866 - precision: 0.0920 - recall: 0.7477 - auc: 0.8953 - prc: 0.3872 - val_loss: 0.0293 - val_tp: 68.0000 - val_fp: 271.0000 - val_tn: 45219.0000 - val_fn: 11.0000 - val_accuracy: 0.9938 - val_precision: 0.2006 - val_recall: 0.8608 - val_auc: 0.9560 - val_prc: 0.6739\n",
      "Epoch 12/100\n",
      "90/90 [==============================] - 1s 14ms/step - loss: 0.5163 - tp: 253.0000 - fp: 2963.0000 - tn: 178992.0000 - fn: 68.0000 - accuracy: 0.9834 - precision: 0.0787 - recall: 0.7882 - auc: 0.9050 - prc: 0.3596 - val_loss: 0.0356 - val_tp: 68.0000 - val_fp: 357.0000 - val_tn: 45133.0000 - val_fn: 11.0000 - val_accuracy: 0.9919 - val_precision: 0.1600 - val_recall: 0.8608 - val_auc: 0.9594 - val_prc: 0.6737\n",
      "Epoch 13/100\n",
      "90/90 [==============================] - 1s 10ms/step - loss: 0.4070 - tp: 269.0000 - fp: 3374.0000 - tn: 178581.0000 - fn: 52.0000 - accuracy: 0.9812 - precision: 0.0738 - recall: 0.8380 - auc: 0.9259 - prc: 0.3373 - val_loss: 0.0394 - val_tp: 68.0000 - val_fp: 406.0000 - val_tn: 45084.0000 - val_fn: 11.0000 - val_accuracy: 0.9908 - val_precision: 0.1435 - val_recall: 0.8608 - val_auc: 0.9670 - val_prc: 0.6659\n",
      "Epoch 14/100\n",
      "90/90 [==============================] - 1s 13ms/step - loss: 0.4262 - tp: 263.0000 - fp: 3760.0000 - tn: 178195.0000 - fn: 58.0000 - accuracy: 0.9791 - precision: 0.0654 - recall: 0.8193 - auc: 0.9220 - prc: 0.3279 - val_loss: 0.0434 - val_tp: 68.0000 - val_fp: 454.0000 - val_tn: 45036.0000 - val_fn: 11.0000 - val_accuracy: 0.9898 - val_precision: 0.1303 - val_recall: 0.8608 - val_auc: 0.9659 - val_prc: 0.6508\n",
      "Epoch 15/100\n",
      "90/90 [==============================] - 1s 14ms/step - loss: 0.4416 - tp: 262.0000 - fp: 4043.0000 - tn: 177912.0000 - fn: 59.0000 - accuracy: 0.9775 - precision: 0.0609 - recall: 0.8162 - auc: 0.9161 - prc: 0.2982 - val_loss: 0.0468 - val_tp: 68.0000 - val_fp: 518.0000 - val_tn: 44972.0000 - val_fn: 11.0000 - val_accuracy: 0.9884 - val_precision: 0.1160 - val_recall: 0.8608 - val_auc: 0.9667 - val_prc: 0.6507\n",
      "Epoch 16/100\n",
      "90/90 [==============================] - 2s 20ms/step - loss: 0.3844 - tp: 269.0000 - fp: 4516.0000 - tn: 177439.0000 - fn: 52.0000 - accuracy: 0.9749 - precision: 0.0562 - recall: 0.8380 - auc: 0.9319 - prc: 0.2704 - val_loss: 0.0518 - val_tp: 68.0000 - val_fp: 573.0000 - val_tn: 44917.0000 - val_fn: 11.0000 - val_accuracy: 0.9872 - val_precision: 0.1061 - val_recall: 0.8608 - val_auc: 0.9697 - val_prc: 0.6294\n",
      "Epoch 17/100\n",
      "90/90 [==============================] - 2s 23ms/step - loss: 0.4173 - tp: 264.0000 - fp: 4796.0000 - tn: 177159.0000 - fn: 57.0000 - accuracy: 0.9734 - precision: 0.0522 - recall: 0.8224 - auc: 0.9219 - prc: 0.2754 - val_loss: 0.0544 - val_tp: 68.0000 - val_fp: 594.0000 - val_tn: 44896.0000 - val_fn: 11.0000 - val_accuracy: 0.9867 - val_precision: 0.1027 - val_recall: 0.8608 - val_auc: 0.9706 - val_prc: 0.6160\n",
      "Epoch 18/100\n",
      "90/90 [==============================] - 3s 28ms/step - loss: 0.4593 - tp: 265.0000 - fp: 4963.0000 - tn: 176992.0000 - fn: 56.0000 - accuracy: 0.9725 - precision: 0.0507 - recall: 0.8255 - auc: 0.9059 - prc: 0.2418 - val_loss: 0.0552 - val_tp: 68.0000 - val_fp: 601.0000 - val_tn: 44889.0000 - val_fn: 11.0000 - val_accuracy: 0.9866 - val_precision: 0.1016 - val_recall: 0.8608 - val_auc: 0.9698 - val_prc: 0.6031\n",
      "Epoch 19/100\n",
      "90/90 [==============================] - 2s 25ms/step - loss: 0.3997 - tp: 269.0000 - fp: 4818.0000 - tn: 177137.0000 - fn: 52.0000 - accuracy: 0.9733 - precision: 0.0529 - recall: 0.8380 - auc: 0.9234 - prc: 0.2328 - val_loss: 0.0560 - val_tp: 68.0000 - val_fp: 605.0000 - val_tn: 44885.0000 - val_fn: 11.0000 - val_accuracy: 0.9865 - val_precision: 0.1010 - val_recall: 0.8608 - val_auc: 0.9721 - val_prc: 0.6031\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00019: early stopping\n"
     ]
    }
   ],
   "source": [
    "# TODO 5\n",
    "# Train the model with class weights.\n",
    "weighted_model = make_model()\n",
    "weighted_model.load_weights(initial_weights)\n",
    "\n",
    "weighted_history = weighted_model.fit(\n",
    "    train_features,\n",
    "    train_labels,\n",
    "    batch_size=BATCH_SIZE,\n",
    "    epochs=EPOCHS,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=(val_features, val_labels),\n",
    "    # The class weights go here\n",
    "    class_weight=class_weight) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R0ynYRO0G3Lx"
   },
   "source": [
    "### Check training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "BBe9FMO5ucTC"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fnzuAH4zKYEV+MRc9MZcglzkgQX7o4sFMHtiVr9YX8IOnv/EmyA08e9UJTOrTic9X7+L6FxYcEutr149mRFYCHyzbzh2vLTmk/b+3jaN/SixvLszl1++sOKT9yzsnkpEYyYfLd/DAR6sPaT+lb2fCgt18s7GQJ77cSGSIm4iQICJC3USGBFFT58HtcpORGMH4nknethA3kaHen9ZajDG8uTCPuRsL+dU7yzmtfxfOG5rKuB5JBLk7/BflEqCMPfhfY4DLycmx8+fPb9Fz/uw/S5i1bjff3HtKi55XRORgxpgF1tocp+NoLf7os50yc+1u3lyYy2erd1FaWUtkiJtzh6byh/MGHvU5aus87CmvYU95NUVl1WQkRtA1NpxtReU8N2czdR6LyxhcBlwuw5QhKfRPiWVrYTmvL9iGMeaA9jMHdiUzKZLNBWV8umonAMYYDGAMTB7QlS6xYWzYvY856wugQRt42xMiQ1i/ax+Lt+0l2G0Idrt8F8OIrAQiQoLYVVLJrtIqgt0ugtyGEN/PTtFhuF0Gj8dijPe5j4W1loVb9/LmwlzeX7qd4ooaLj2hG3+6YNAxnU+kpTTVZyuBBv7x2Tr++slaVv32dMJD3C16bhGRhpRAtx3FFTXMWLOLswal4HYZfvPeCt5alMepfTtz+oAujO2RRFiw/s9oaVW1dXyxejddY8MY3C2O9bv2ccvLCzlvaCpThqTSJfbop7yIHK+m+mxN4QAykryl7LYWldO7S7TD0YiIiFN2l1bxycqdfLRiB3PWF1DrsaTGhZOTmcDtp/biF2f01bQCPwsNcnP6gC71t0srawgPcfPHD1fzp49WM65HEucNTeWMgV31B4w4Rgk0kOGrxLG5sEwJtIhIB1PnsbhdhkVb93D+P+dgrXdtzA/HZ3Fa/y4MSYsDICYs2NlAO6ih6fG8ddNYNhWU8daiPN5alMvdby7jZN/86+LyGmLCg455+ojIsVACDWT6NlPZqoWEIiLtnrWW1TtK+XjFDj5esZMTeyVz9+Q+DEiN5Y5TenFKv8706RKthCzAZCVFcsepvbj9lJ5sLCgjNtz7B80Vz86jzuPhytGZnD04RaPS0iqUQAOxEcHEhgdrN0IRkXbub5+s5a1FeWwtKscYyMmIp1fnKACC3S5uPbmnwxHKkRhj6J7s/Z15PJaLc9L495zN3Pn6Uv744WqmjujGD0Zl1FdAEfEHvyXQxphuwPNAF8ADTLPWPnzQMROBd4BNvrvetNb+1l8xHU6mStmJiLQr1bUe5m4sZOGWPdx+ai/Au9YlKymSG07szqn9OpMcHepwlHI8XC7DZSMz+P6IdOZuKOTZOZt5fMYG0uIjmDoindo6D26X0bcJ0uL8OQJdC/zUWrvQGBMNLDDGfGKtXXnQcbOstWf5MY6jkp4YyZJte50OQ0REjkNtnYdPV+3iw+Xb+dxXbi4ixM3lozNIigrloYsHK5lqh4wxjOmRxJgeSWwrKicpyvuH0Ytfb+G1+blcNTaTczS9Q1qQ3xJoa+12YLvveqkxZhWQChycQAeEjIQIPli2nZo6D8FaYS0i0ia9tSiPO19fSnxEMKf378Jp/bswrud35eaUPLd/3XyFAQA6x4RR57H8/PWl/PGDVUwdkc4PRmWQEqfpHXJ8WmUOtDEmExgKfNNI82hjzBIgH/iZtfbQrZBaQUZiBHUeS96eCjJ9Ze1ERCSw7Sqp5Lk5m8lOjuLC4WmcPTiFuIgQJvVOVrk5YfLArpw+oAtzNxby3FebeeLLDSzYsodXrx/tdGjSxvk9gTbGRAFvAD+x1pYc1LwQyLDW7jPGnAG8DRyygsMYcx1wHUB6erpf4szwVeLYXFimBFpEJMCt21nKtJkbeWdxPjUeD9eMzQIgLNjNqf06OxydBBJjDGO6JzGmu3d6R0mldzv13aVVTHpwBrUej/c4vN9O/P7cAVwwPI3F2/Zy2b++rj+H9xh48OLBnNa/C0u27eUXby8jLS6CbgnhpMVHkBYfzvCMeOIiQlr/hUqr8msCbYwJxps8v2StffPg9oYJtbX2A2PM48aYJGttwUHHTQOmgXdXK3/Empno/cpna5EWEoqIBLI/fbiaJ77cQFiwi0tHdOOH47LqB0FEDqfh9I7dpVVcNtI7KNcwsejRyVvhIykqhKkj0uvb9m/cnBb/3fSPpKhQ1u0q5Ys1u6iq9Sbir10/mhFZCXyyciePfLaOtPhw38WbYI/KTiQyVEXQ2jp/VuEwwNPAKmvtQ00c0wXYaa21xpgRgAso9FdMh5McHUp4sFuVOEREAkxtnYcPlu9gTPdEkqJCGZWd4F0YOCqD+EiN9Mmx6ZcSQ7+UmCbb0+Ij+OVZ/ZpsH9wtjueuHgF4a4sX7Ksmd085vTp7N2QLCXKREBnC2p2lfL76uwR71s8nERkaxPtL8/lqfQEjsxIZkZWgedltjD//BBoLXA4sM8Ys9t13L5AOYK19ArgQuNEYUwtUAJdaa/0ywnwkxhgyEiPYUqha0CIigWBfVS2vfruNZ2ZvIm9vBb88sy/Xjs9mYu9OTOzdyenwROoZY0iODj2gLOKJvZI5sVcycGCC3TU2DIC8PRW8v3Q7r8zbBnhHtkdnJ/LABYNwubTYNdD5swrHbOCwnwBr7aPAo/6KobnSEyLYVKAEWkTESdZaHvzfGl6Yu4WSylpGZCbwm3P6c1IfJc3SNjWWYF9/YneuHZ/N6h0lzNtUxDcbi8gvrqhPnn/2nyVU1XoYmZXAyKwEenSKUhWZAKJJOA1kJkUyY+1uPB6rv/5ERBxijGHDrjLG9UziR+OzGZoe73RIIn7hdhn6p8TSPyWWq30LYfcLdhtmri3kvSX5ACREhvCDURnccWov6jyWbzY2mPHqS1m6xUfQLSGC6loPi7bu8TbtXwBpvKPc2qGxZSiBbiDd96HbWVqpD5iIiIMeu2wYbg1kSAf2x/MHYa1la1E532ws4ptNRcRHBAPeXTa//9ShlYFvO6kHd3yvN8UVNVwy7etD2u+e3IcbTuxOUVk1f/tkLWcN6soJmQkaNDwGSqAbyPBV4thcUK4EWkTEQUqeRfavz4okIzGSi0/oVn9/SJCLV68bBRxYQSTVtxAxNjyYl68deUgFkf15zsr8Ev6zYBsvfL2FzjGhnDGwK2cNSmFYepymiRwlJdANZPrKIG0tKmN090SHoxERERE5lNtlGJnddJ4SEuRiTI+kJtvH9UxiwS9P5bPVu3h/ST4vfb2VZ7/azKyfT6JbQgT7qmqJDHErmT4MJdANdI0NI8hlVMpORERE2rXI0CDOGZzCOYNTKKms4ZuNRfV1sn/62mLW7CjlrEEpnDW4K707RyuZPogS6AaC3C66JUQogRYREZEOIyYs+IAdPE/r34Wyqjoen7GeR79YT49OUVw9NpPLRmY4GGVgUQJ9kPSECLYUqZSdiIiIdEznD0vj/GFp7C6t4qPl23lv6XZ2lVQBUF5dy91vLGNItziGpMfRPyWG0CC3wxG3PiXQB8lMjGDhlj1Ya/V1hYiIiHRYydGhXD46k8tHZ7J/n7u8PRV8u7mId33l9ULcLvqmxHD36X0Y3T2ROo/FZWj3OZQS6IOkJ0ZSWlXLnvIaErRFrIiIiEh9QtyzczRz7zmZHcWVLN62h0Xb9rJo614iQ72j0B+v2MGv3l7uHaHuFsfQ9HgGdYslJizYyfBbnBLog2TuL2VXWKYEWkRERKQRXWLDOD22K6cP6HrI/Sf16cSibXv5bPUuwLuJy+y7TiI1Lpw1O0qprKmjT9foNj31Qwn0QfbXSNxaWM4w7X4lIh2IMeZ04GHADTxlrf1TI8dMBP4OBAMF1toTWzFEEQlww9Lj6/On4ooalubuZXleCSmxYQA8OXMDby7MI9ht6Ns1hkFpsQzpFs+Fw9OcDLvZlEAfJC0+AmNQJQ4R6VCMMW7gMeBUIBf41hjzrrV2ZYNj4oDHgdOttVuNMZ0cCVZE2oTY8GDG90xmfM/k+vvuPK03p/TtzJLcvSzdVsw7i/L5emNRfQL9xw9WUeuxDEqLZXBaHBmJEQE5n1oJ9EHCgt10jQljS6EqcYhIhzICWG+t3QhgjJkOTAFWNjjm+8Cb1tqtANbaXa0epYi0aV1jw+k6MJwzBnqnfng8lsKy6vr29bv2MXt9AVW1HgBiwoKYOiKde87oC8Cjn6+r31lxv8Hd4pjQK5maOg/TZm485DmHZ8Qz6jAbzxwLJdCNSE+MYEuRRqBFpENJBbY1uJ0LjDzomF5AsDFmBhANPGytfb51whOR9sjlMiRHh9bffvqqE6ip87B2ZynLcotZkltMV9/0D4CHPlmL56AE+uqxmUzolUxtneUvH6855DluPamHEujWkJkYyaerdjodhohIa2rsO9KD/psiCBgOnAyEA3ONMV9ba9cecCJjrgOuA0hPT/dDqCLSngW7XfRPiaV/SiyXjjiwbe3vJx9y/P4pHmHBrkbbXX6YAaIEuhHpiREU7KtmX1UtUaF6i0SkQ8gFujW4nQbkN3JMgbW2DCgzxswEBgMHJNDW2mnANICcnJyDk3ARkWMW5HY12WaMISSodeZLKztsRGZiJABbCsvonxLrcDQiIq3iW6CnMSYLyAMuxTvnuaF3gEeNMUFACN4pHn9r1SjlENuLK3hrUR4r80uoqfNQU2d9Pz3U1l/3/qz1WKprPdR6vG3VvmNCglz86qx+ba4SgohTlEA3Ij3hu1J2SqBFpCOw1tYaY24BPsZbxu4Za+0KY8wNvvYnrLWrjDEfAUsBD95Sd8udi7rjKquq5eMVO3hzYR5fbSjAWm8Z1rAgN0FuQ7DbRbDbEBLkIiI0iBC3IcjlIjjIRbDL297wuMXb9vKz/yxh/a59/Py03rj88Z23SDuiBLoR+2tBayGhiHQk1toPgA8Ouu+Jg27/BfhLa8YlXh6PZe7GQt5YmMtHy3dQXl1Ht4RwbjupJ+cNTSUzKfKYz11T5+H+d1fwxJcb2Lh7H3+7ZAiRmsIo0iT962hEdFgwiZEhKmUnIiKOW79rH28uzOXtRXnkF1cSHRrEOYNTOH9YGidkxrdIjdxgt4vfnzuAHp2i+N37K7nwibk8fWUOKXHhLfAKRNofJdBNSE+M0GYqIiLiiKKyat5bks+bC3NZkluM22WY0DOJe87oy6n9OhMW3PJbIBtjuHpsFplJkdz68iKmPPYV/7oihyHd4lr8uUTaOiXQTchMjGTepiKnwxARkQ6iutbD56t38ebCXL5Ys4uaOkvfrjH88sy+nDMkhU7RYUc+SQuY1LsTb940hh/++1sueXIuf7loMOcMTmmV5xZpK5RANyE9IYK3F+dRVVtHaFDL/6UvIiKy37aici6d9jV5eytIjg7lqjGZnDc0jX4pMY7E06tzNG/fNJYbXlzAba8sYv2ufdx+Ss+A3FJZxAlKoJuQmRSBtbCtqIIenaKcDkdERNqpwn1VXPnMPEora3j6yhxO7JV82Fq3rSUxKpQXrx3JvW8u55HP1rFh9z7+etHgFps+sq+qlo+W76C0soarx2a1yDlFWovfEmhjTDfgeaAL3nJH06y1Dx90jAEeBs4AyoGrrLUL/RVTc6QneFczby0qUwItIiJ+UV5dyzX/nk/e3gpeunYkOZkJTod0gNAgNw9eNIienaN44KPV5BaV868rcugUc2zTSWrqPMxcu5u3FuXx6aqdVNZ4AOjbNabFt1oW8Sd/jkDXAj+11i40xkQDC4wxn1hrVzY4ZjLQ03cZCfzT99NxmftL2WkhoYiI+EFNnYebXlrIsty9PHl5TsAlz/sZY7jhxO5kJUVy+6uL6xcXDkg9un0SrLUs3LqXtxfl8d9l2ykqqyY+IpiLhnfjzEFdufWVRTzy2Tol0NKm+C2BttZuB7b7rpcaY1YBqUDDBHoK8Ly11gJfG2PijDFdfY91VEJkCFGhQUqgRUSkxVlrueuNpcxYs5s/nT+QU/t1djqkIzqtfxf+c8NofvTv+Vz0xFz+dskQTh/QpcnjN+zexzuL8nh7cT5bi8oJDXJxar/OnDc0lQm9kgn2TVO5fkI2v//vKuZvLgrYPyJEDtYqc6CNMZnAUOCbg5pSgW0Nbuf67nM8gTbGkJ4QoVrQIiLS4v700WreXJjHHaf24tIR6U6Hc9T6p8Ty9i1j+dHzC7jhxQX8/PTe3Hhi9/rFhbtLq3hvST5vL85jaW4xLgNjeyRx28k9Oa1/Z6LDgg8552UjM/jnjA088vl6nr9mRGu/JJFj4vcE2hgTBbwB/MRaW3JwcyMPsY2c4zrgOoD09NbraDKTIli9vbTVnk9ERNq/p2dv4skvN3L5qAxuPamH0+E0W6foMF69bhR3vr6UP3+0hvW79jG+ZxJvLcrnq/UF1HksA1K95ffOHpxC5yPMlw4PcfOjCdn86cPVLNq6h6Hp8a30SkSOnV8TaGNMMN7k+SVr7ZuNHJILdGtwOw3IP/gga+00YBpATk7OIQm2v6QnRPLJyp3UeSxul0r3iIjI8XlncR6/e38lkwd04f5z+rfZsnBhwW4euXQIPZKj+Nuna3lzYR5p8eHceGJ3zh2aQo9O0c063+WjMnjyyw384/P1PHPVCX6KWqTlHFUCbYyJBCqstR5jTC+gD/ChtbbmMI8xwNPAKmvtQ00c9i5wizFmOt7Fg8WBMP95v8zECGrqLPl7K+iWEOF0OCIi0obNWrebn/1nCSOzEvjbJUPa/MCMMYYfn9KTCb2SqPNYhmcc+7bikaFBXDs+m798vIZlucUMTDu6BYoiTjnaQpMzgTBjTCrwGXA18NwRHjMWuBw4yRiz2Hc5wxhzgzHmBt8xHwAbgfXAv4CbmvsC/CndV4lja5EWEoqIyLFbllvMDS8soHtyFNOuyPHLVtxOGZoeT05mwnGPpl8xOoOYsCAe+XxdC0Um4j9HO4XDWGvLjTE/BP5hrf2zMWbR4R5grZ1N43OcGx5jgZuPMoZWl5norQW9pbCcsW1vmpqIiASAzQVlXPXsPOIiQvj3NSOIDT90IZ1AdFgwPxyXzd8+XcuK/GL6p2gUWgLX0Y5AG2PMaOAy4L+++9r9LoZdYsIICXKpEoeItCnGmJuNMXENbscbYwLqG76OYldpJVc8Mw+PtTz/wxFHXFDX0V01NpPo0CAe/Xy906GIHNbRJtA/Ae4B3rLWrjDGZANf+C2qAOFy7S9lpykcItKm/Mhau3f/DWvtHuBHzoXTMZVW1nD1s9+yu7SKZ646ge7J2tX2SGLDg7l6bCYfLt/Bmh2qgiWB66gSaGvtl9bac6y1DxhjXECBtfY2P8cWEDISItisEWgRaVtcpsGEVGOMGwhxMJ4Op6q2jhteXMDqHaU8/oNhKs3WDNeMyyIyxM0/NBdaAthRJdDGmJeNMTG+ahwrgTXGmDv9G1pgSE+MYGtROd7p2iIibcL/gNeMMScbY04CXgE+cjimDsPjsfz0tSV8tb6QBy4YxKTenZwOqU2JiwjhyjGZ/HfZdtbv0ii0BKajncLRz7cJyrl4K2ek462w0e5lJkZSXl3HzpIqp0MRETlad+KtmHQj3oXanwE/dzSiDsJay+/+u5L3l27n7sl9uHB4mtMhtUnXjs8mPNitudASsI42gQ72bYpyLvCOr/5zhxiSHd09EWPguTmbnQ5FROSIfNPslllrn7DWXmitvcBa+6S1ts7p2DqCJ77cyLNfbeaasVlcPyHb6XDarITIEC4flcG7S/LZuHuf0+GIHOJoE+gngc1AJDDTGJMBHLwtd7vUq3M05w1N5ZmvNpG3t8LpcEREDsta6wGWGGPSnY6lo/B4LF+tL+CmlxbwwEerOWdwCr88s2+b3WUwUFw7PpuQIBePfbHB6VBEDnG0iwgfsdamWmvPsF5bgEl+ji1g/PR7vQF46H9rHY5EROSodAVWGGM+M8a8u//idFDtzZ6yav41cyMnP/Qllz31DXM2FHL9idk8eNFgXG18l8FAkBwdymUjM3h7cZ7KyUrAOdqtvGOB+4AJvru+BH4LFPsproCSGhfO1WMzmTZzIz8cl0W/lBinQxIROZzfOB1Ae2WtZcGWPbz0zVb+u2w71bUecjLiue3kHkwe0LVd7TAYCK6fkM0LX2/h8S828MCFg5wOR6Te0W6G8gywHLjYd/ty4FngfH8EFYhumtiDV7/dxp8+Ws3z14xwOhwRkUMYY8KAG4AewDLgaWttrbNRtQ8llTW8vSiPl77eypqdpUSHBjH1hG58f2QGvbtEOx1eu9UpJoypJ3TjpW+2cstJPeiWEOF0SCLA0SfQ3a21FzS4/RtjzGI/xBOwYsODuWVSD37/31XMXlfAuJ5JTockInKwfwM1wCxgMtAP+LGjEbVxy3KLeembLbyzOJ+KmjoGpcXywAUDOXtwChEh7X5D3oBww8TuvDJvG//8cgP/d95Ap8MRAY4+ga4wxoyz1s4GMMaMBTrcirrLR2fw3JzN/PHDVbzXfZzmuIlIoOlnrR0IYIx5GpjncDxtUnl1Le8tyeelb7ayNLeY8GA3U4ak8P2R6QxKi3M6vA6na2w4F+Wk8dr8bdwyqQcpceFOhyRy1An0DcDzvrnQAHuAK/0TUuAKDXJz52m9+fH0xby7JJ9zh6Y6HZKISEM1+69Ya2tVBaJ5auo8/OOzdTz71WZKq2rp3Tma307pz7lDU4kJC3Y6vA7txondefXbbTz55QZ+M2WA0+GIHF0Cba1dAgw2xsT4bpcYY34CLPVjbAHp7EEp/GvWRv7y8RpOH9BFC0ZEJJAMNsbsLzFqgHDfbQNYa61WQDdhW1E5t76yiMXb9nLmoK5cMzaTYenxKkUXINLiI7hweBqvfLuNmyb1oHNMmNMhSQd3tHWgAW/i7NuREOAOP8QT8Fwuw72T+5K3t4IX5m5xOhwRkXrWWre1NsZ3ibbWBjW4ruS5Ce8vzeeMh2exYfc+Hvv+MB77/jCGZyQoeQ4wN03sQZ3H8uSXG50ORaR5CfRBOmzPMqZHEhN7J/OPz9ext7za6XBEROQYVFTXcc+bS7nl5UX06BzFB7eN58xBXZ0OS5qQnhjBeUNTeembLewqrXQ6HOngjieB7hBbeTfl7sl9KK2q5fEZ2iFJRKStWbOjlHMenc30b7dx48TuvHb9aJVIawNuntSDmjoPT83a5HQo0sEdNoE2xpQaY0oauZQCKa0UY0Dq0yWGC4al8dxXm8ndU+50OCIichSstbz0zRbOeXQ2e8preP6aEdx1eh+C3cczniStJSspkilDUnlh7hYK91U5HY50YIftMfbPm2vkEm2t7fAFMO84tRfGaItvEZG2oLi8hpteWsgv3lrOyOxEPvzxeMb3THY6LGmmmyf1oLK2jqdmaxRanKM/uY9DSlw414zL4q3FeSzP6xC7mouItEkLtuzhjEdm8cnKndwzuQ/PXXUCydGhToclx6BHpyjOGpTC83M2s6dM65DEGUqgj9ONE7sTFx7MAx+tdjoUERE5SJ3H8tgX67n4ybm4XPD6jWO4/sTu2girjbv1pB6UVdfxzFcahRZnKIE+TjFhwdx6Uk9mrStg5trdTocjIiI+u0oqueKZb/jLx2uYPKAL/71tPEO6xTkdlrSAXp2jOWNgF577arOqYYkjlEC3gMtGpdMtIZw/friaOk+HLk4iIhIQZqzZxeSHZ7Fgyx4euGAg/5g6VLsJtjO3ntSTsupaLnxiLqt3lBz5ASItSAl0C/Bu8d2HVdtLeHtRntPhiIh0WNZa/vjhKq569luSo0N575ZxXHJCujZFaYf6do3h39eMoLiihnMe/YoX5m7GWg1iSetQAt1CzhrYlUFpsfz1f2uorKlzOhwRkQ7prUV5PPnlRqaOSOftm8fSs3O00yGJH43vmcyHPx7P6OxEfvXOCq5/YYGmdEir8FsCbYx5xhizyxizvIn2icaYYmPMYt/l1/6KpTW4XIa7J/chv7iSf8/Z7HQ4IiIdTnFFDf/3wSqGdIvjD+cOICzY7XRI0gqSokJ59qoT+OWZffnCN3Xnm42FTocl7Zw/R6CfA04/wjGzrLVDfJff+jGWVjGmexIn9enEo1+sV2kdEWlzjDGnG2PWGGPWG2PuPsxxJxhj6owxF7ZmfEfyt0/WUlhWze/PHaAqGx2My2W4dnw2b9w4htAgF1P/9TV/+2QttXUep0OTdspvCbS1diZQ5K/zB6q7Tu9DWVUtj32x3ulQRESOmjHGDTwGTAb6AVONMf2aOO4B4OPWjfDwVuaX8PzczfxgZAYDUmOdDkccMigtjvdvG8+UIak8/Nk6vv+vb8jfW+F0WNIOOT0HerQxZokx5kNjTH+HY2kRvbtEc+HwNJ6fu4VtRdriW0TajBHAemvtRmttNTAdmNLIcbcCbwC7WjO4w7HW8ut3lhMXEcLPvtfb6XDEYVGhQfztkiE8dPFgVuQXM/nhWXy8YofTYUk742QCvRDIsNYOBv4BvN3UgcaY64wx840x83fvDvxay7ef2guXCx783xqnQxEROVqpwLYGt3N999UzxqQC5wFPtGJcR/Tmwjzmb9nD3af3ITZCperE6/xhabx/23jSEyK4/oUF/Ort5VrkLy3GsQTaWltird3nu/4BEGyMSWri2GnW2hxrbU5ycnKrxnksusaG88NxWbyzOJ9ludriW0TahMYmDR9cE+zvwF3W2sNmIa056FFcUcMfP1zF0PQ4Lhye5tfnkrYnKymSN24cw7Xjsnjh6y2c+9hXrNtZ6nRY0g44lkAbY7oYX2FOY8wIXyztZtns9Sd2JyEyhD9+uEp1KUWkLcgFujW4nQbkH3RMDjDdGLMZuBB43Bhz7sEnas1Bj799spaismp+N0ULB6VxIUEufnlWP569+gR2l1Zx9qOzeWXeVv3fLMfFn2XsXgHmAr2NMbnGmB8aY24wxtzgO+RCYLkxZgnwCHCpbUef5piwYG47qQdzNhTyycqdTocjInIk3wI9jTFZxpgQ4FLg3YYHWGuzrLWZ1tpM4HXgJmvt260eqc+K/GLvwsFRWjgoRzapdyc+/PF4cjISuOfNZdzy8iKKK2qcDkvaqCB/ndhaO/UI7Y8Cj/rr+QPB90dmMP3bbdw2fRHTLs9hQq/An34iIh2TtbbWGHML3uoabuAZa+2K/YMe1tqAmvfs8Vjue2cF8REh/PRULRyUo9MpJoznrxnBkzM38tf/rWHWut18f2QGV47JoGtsuNPhSRvidBWOdi0kyMWL144kMzGSa/89n081Ei0iAcxa+4G1tpe1tru19g+++55oLHm21l5lrX299aP0enORd+HgXZO1cFCax+Uy3DixO2/fPJZxPZOYNnMD4x/4gh9PX8TS3L1OhydthBJoP0uKCmX6daPo0zWaG15cwAfLtjsdkohIm1ZcUcMfP1jFsPQ4LhymhYNybAakxvL4ZcP58s5JXDkmk89W7eKcR7/i4ifm8tHyHdR52s2sUvEDJdCtIC4ihBevHcngbnHc8vJC3l6U53RIIiJt1kP/W8Oe8mp+q4WD0gK6JUTwq7P6Mfeek/jlmX3J21vBDS8uYNKDM3j2q03sq6p1OkQJQEqgW0lMWDDPXzOCkVmJ3P7aYl79dqvTIYmItDnL84p54estXK6Fg9LCosOCuXZ8Nl/eOZHHLxtGUlQIv3lvJaP/+Bn/98Eq8rSjoTTgt0WEcqjI0CCevfoErnthAXe9sYyqWg9XjM50OiwRkTbB4/HuOBgfEcId2nFQ/CTI7eKMgV05Y2BXFm7dw9OzN9VfJg/owrXjsxnSLc7pMMVhSqBbWViwm39dMZybX1rEr99ZQVWNhx9NyHY6LBGRgPfGwlwWbt3LXy4cRGy4Fg6K/w1Lj2fY9+PJ21vBv+ds5pVvtvL+0u0Mz4jnmrFZnNKvE6FBbqfDFAcogXZAaJCbf/5gGD+Zvpg/fLCKypo6bj25p9NhiYgErOLyGv704WqGZ8RzgRYOSitLjQvn3jP6ctvJPXnt2208O2cTN7+8kJiwIM4clMJ5Q1PJyYjXnPwORAm0Q4LdLh6+dAihQS7++slaKmvr+Nn3euPbnFFERBr46yfehYPPTxmhJEUcExUaxDXjsrhyTCYz1+3mnUV5vL0oj1fmbSU1LpwpQ7zJdM/O0U6HKn6mBNpBQW4XD140mJAgF499sYHKGg+/PLOvkmgRkQaW5xXz4tdbuGJ0Jv1TtHBQnOd2GSb17sSk3p0oq6rlk5U7eWtRHk98uYHHZ2ygf0oM5w5J5ZwhKXSOCXM6XPEDJdAOc7kM/3feQEKDXDw9exNVtXX89hyVZhIRAe/CwV+9s5yEyBBuP7WX0+GIHCIyNIhzh6Zy7tBUdpdW8f7SfN5elMcfPljF/324irHdk5gyJIXTB3QhOkxz99sLJdABwOUy3H9Of8KC3Tw5cyNVNR7+dMEg3EqiRaSDe31hLou27uXBiwZr4aAEvOToUK4em8XVY7PYuHsfby/2JtN3vr6UX769nFP7debcIalM6JVMSJAqCbdlSqADhDGGuyf3ITTYzSOfraO6zsNfLxpMkFv/wESkY9q/cDAnI57zh6Y6HY5Is2QnR3HHqb24/ZSeLNy6l7cX5fH+0nzeX7qd+IhghqbHk5UUSXZyJNlJUWQnR9IpOlTTONsIJdABxBjDHaf2IjTIxV8+XkNVjYdHpg7VX6ki0iE9+L817C2v5rdTRmpam7RZxhiGZ8QzPCOeX5/dj5lrd/PfpdtZub2EORsKqKzx1B8bFRpEVlJkfWKdlRRJ9+QospIiiQxVyhZI9NsIQDdP6kFYsJvfvb+Sm15awBM/GK6RaBHpUJblFvPiN1u4cnQm/VJinA5HpEUEu12c3LczJ/ftDHjn+G8vqWTj7n1sKihj4+4yNhaUsXDrHt5bmo+13z22c0wo2UlRZCVHMjw9nrMHp2iAzUFKoAPUD8dlEeQy3PfuCn759nL+eP5Afa0jIh3C/oWDiZGhWjgo7ZrLZUiNCyc1LpzxPZMPaKusqWNLYTkbd+9joy+53lSwjw+Wbeflb7by4P/WcN2EbC49IZ3wEG3m0tqUQAewK8dksrOkksdnbCA1LlybrYhIh/D6glwWb9vLX7VwUDqwsGA3vbtE07vLgTWlrbXMXFfAY5+v5zfvreTRz9dzzbgsLh+dQYyqfLQaJdAB7s7TepO/t4K/frKWlLhwLhiuHbhEpH1LiQvn/GGpnD9MCwdFDmaM4cReyZzYK5l5m4p4fMZ6/vLxGp6YsYErxmRwzdgsEqNCnQ6z3VMCHeCMMfz5wsHsLKnirjeW0iU2jLE9kpwOS0TEb8b1TGJcT/VzIkcyIiuBEVkjWJ5XzOMz1vP4jA08PXsTU0ek86Px2aTEhTsdYrul2edtQEiQiycuH052ciQ3vLCA1TtKnA5JREREAsSA1Fgev2w4n9x+ImcNSuGFuVs48S9fcNfrS9lUUOZ0eO2SEug2IjY8mOeuHkFEqJurnvmW7cUVTockIiIiAaRHpygevGgwM+6cyNQR6by1OI+T/zqDW19ZxKrtGnxrSUqg25CUuHCevWoE+6pqufrZbymprHE6JBEREQkwafER/HbKAGbfNYnrJnTni9W7mPzwLH743Lcs2LLH6fDaBSXQbUy/lBj++YNhrN+1jxtfXEB1refIDxIREZEOp1N0GHdP7sNXd53ET0/txcKte7jgn3O46Ik5fLR8B3Uee+STSKOUQLdB43sm88fzB/LV+kLufnMp1uofgIiIiDQuNiKYW0/uyVd3n8R9Z/djR0klN7y4gJP+OoN/z9lMeXWt0yG2OUqg26iLcrpx+ym9eHNhHg99stbpcERERCTARYQEcfXYLGb8bBL/vGwYiZEh3PfuCkb/8XMe+Gg1O0sqnQ6xzfBbGTtjzDPAWcAua+2ARtoN8DBwBlAOXGWtXeiveNqj207uQf7eCv7x+XpS4sKZOiLd6ZBEREQkwLldhskDuzJ5YFcWbNnD07M38uSXG3hq1kbOHpzCteOy6ZcS43SYAc2fdaCfAx4Fnm+ifTLQ03cZCfzT91OOkjGG3583gO0llfzy7eV0iQ1jUu9OToclIiIibcTwjHiGZwxna2E5z87ZxKvfbuPNhXmM7ZHIteOyObFXMi6XcTrMgGP8OX/WGJMJvN/ECPSTwAxr7Su+22uAidba7Yc7Z05Ojp0/f74/wm2z9lXVcsmTc9lUUMZr149mQGqs0yGJSBOMMQustTlOx9Fa1GeLtC3FFTVMn7eVZ7/azI6SSnp0iuKH47I4b2gqYcHuIz7eWktpVS1F+6opLKumqKyaPWXe61Ghbi4c3o3wkCOfJ1A01Wc7mUC/D/zJWjvbd/sz4C5r7WF7WnXGjdtVUsl5j8+hus7DmzeOoVtChNMhiUgjlECLSFtQU+fhv0u3869ZG1mRX0JiZAg/GJVB7y7RFPqS4qKy/UlyFYX7qtlT7r2vpq7p3LJzTCi3n9KLC4enEeQO/KV4TfXZTm7l3dj3AY2+48aY64DrANLTNc+3MZ1iwnju6hO44J9zuPq5b3njhjHERgQ7HZaIiIi0QcFuF+cOTWXKkBS+3ljE07M38vBn6w44JiYsiMSoUOIjgkmLj2BwWhwJUSEkRoYQHxFSfz0hMoTEyFCW5RXzpw9Xcfeby3hq9iZ+flpvTu3XGe+yuLZFUzjama83FnLF0/MYkh7HCz8cQWhQ2/maRKQj0Ai0iLRV24rK2VdV602QI0MIPoYRZGstH6/YyZ8/Xs3G3WUMz4jnnsl9yMlM8EPEx6+pPtvJsfN3gSuM1yig+EjJsxzZqOxEHrx4MPM2FfHT15aoSLqIiIi0iG4JEfTtGkOnmLBjSp7BWwDh9AFd+N9PJvB/5w1kW1E5Fz4xlx89P5/1u0pbOGL/8WcZu1eAiUCSMSYXuA8IBrDWPgF8gLeE3Xq8Zeyu9lcsHc05g1PI31vBnz5czfpd+/jFmX0Z3zPZ6bBEREREAAhyu/j+yHTOHZrCM7M38cSXG/ne32ZycU43fnJKL7rEhjkd4mH5dQqHP+jrwKNjreWj5Tv444er2VpUzkl9OnHvGX3o0Sna6dBEOjRN4RAROVRRWTWPfr6eF77ejNtluGZsFtef2J3YcGfXczlShcMf1Bk3T1VtHf+es5l/fLae8po6fjAynR+f0ouEyBCnQxPpkJRAi4g0bVtROX/93xreXpxPXEQwt0zqweWjMxxb06UEuoMr3FfF3z5dy8vfbCUyNIgfn9yTK0ZnEhIU+CVkRNoTJdAiIke2PK+YBz5azax1BaTGhXPHqb04sXcyiZEhrVq1Qwm0ALB2Zyn/98EqZqzZTUZiBPdM7stp/dtmCRmRtkgJtIjI0Zu9roA/fbSK5XklAESHBpGVHElmYiSZSZFkJUWQmRhJdlKUX8r3KoGWA3y5dje/f38l63btY2RWAr86q592MBRpBUqgRUSax+OxzNlQyJqdpWwuKGNzYRmbCsrI21tBwzQ2PiLYm1QnRpKVtD/B9v6MCj22uhlKoOUQtXUepn+7jb99spai8mrOH5rGnaf1DviVryJtmRJoEZGWUVlTx7aicjbVJ9Xl9Qn29uLKA449pW8nnrryhGY/RyDuRCgOC3K7+MGoDM4ZksJjX6zn2dmb+WDZdq4/MZvrJmQTEaKPh4iIiASmsGA3PTtH07PzoRXGKqrr2FxYxuaCMjYVlpEUGdqiz60MSYgJC+aeyX25bEQGD3y0mr9/uo7p87Zx5ZhMTuyVTJ8u0bhcmiMtIiIibUN4iJu+XWPo2zXGL+dXAi310hMjeOyyYVy9uYg/friaBz7yXpKiQhnfM4nxPZMY1zOJTtGa4iEiIiIdlxJoOUROZgJv3DiGnSWVzFpXwKx1u5m5djdvLcoDoE+XaCb0SmZ8zyROyEwgLNiZ2owiIiIiTlACLU3qHBPGhcPTuHB4Gh6PZeX2EmatK2Dm2t08+9Umps3cSGiQixFZCUzomcz4Xkn07hytkngibZQx5nTgYcANPGWt/dNB7ZcBd/lu7gNutNYuad0oRUScpwRajorLZRiQGsuA1FhunNid8upavtlYxMx1u5m1roA/fLAKPoDkaO90j6Hp8QT75k0bAwZfUt3gx/5Ee3+6bYz34na5GN8jiXjtlijSaowxbuAx4FQgF/jWGPOutXZlg8M2ASdaa/cYYyYD04CRrR+tiIizlEDLMYkICWJSn05M6tMJgPy9FcxeV8DMdbv5fPUu3lyYd1znjwxxc/noTK4dn0VSVMuunBWRRo0A1ltrNwIYY6YDU4D6BNpaO6fB8V8Daa0aoYhIgFACLS0iJS6ci0/oxsUndKPOY9ldWoXF1hc4t8D+muMHlx7/7hjvlT3lNTwzexPTZm7guTmbuGxkBtdPyKZTjBYvivhRKrCtwe1cDj+6/EPgQ79GJCISoJRAS4tzu8xxbcaSkQiPTB3Kj0/pyWNfrOe5OZt54estTD2hGzdM7E7X2PAWjFZEfBpbvNDoTlvGmEl4E+hxTbRfB1wHkJ6e3lLxiYgEDJfTAYg0pXtyFA9dPITPf3oi5w1J5aVvtnLin2dw71vL2FZU7nR4Iu1NLtCtwe00IP/gg4wxg4CngCnW2sLGTmStnWatzbHW5iQnJ/slWBERJymBloCXkRjJAxcOYsadE7koJ43X5+cy6cEZ/Pz1JWwuKHM6PJH24lugpzEmyxgTAlwKvNvwAGNMOvAmcLm1dq0DMYqIBARN4ZA2Iy0+gj+cN5BbTurBk19u5JV5W3l9QS7nDknlpkk96NEpyukQRdosa22tMeYW4GO8ZeyesdauMMbc4Gt/Avg1kAg87quiU2utzXEqZhERpxh78IquAJeTk2Pnz5/vdBgSAHaVVvKvmRt58eutVNbWcdagFG6Z1IPeXaKdDk2kScaYBR0p6VSfLSJtWVN9tkagpc3qFB3GL87sx/UnduepWZt4Ye5m3luSz5BucWQmRpCeEEG3BO/P9MQIOkeH4XJpkxcRERE5Pkqgpc1Ligrl7sl9uH5CNs/N2cw3mwr5dvMe3l2Sj6fBFywhbhdp8eHfJdW+BLtbQjjpCRFEhwU79yJERESkzVACLe1GfGQIt5/aq/52da2H/L0VbC0qZ2tROdv2lLPNd33R1j2UVNYe+PiIYNLiI0iMCiEhMoTEyBASIkNJjAxpcF8oCVEhRIa4tWW5iIhIB6UEWtqtkCAXmUmRZCZFNtpeXF7Dtj3l9Qn21qJy8vZUULivmnU791Gwr4qqWk+T5/Ym2N8l24lRoZyQmcCkPsmEBrn9+dJERETEQUqgpcOKjQgmNiKWAamxjbZbaymvrqOorJrCsmqKyqoo3FdNUVl1/X2F+6ooKqtmU0EZBfuqeHr2JqLDgjhjQFemDE1hZFYibs27FhERaVeUQIs0wRhDZGgQkaFBdEuIOOLxtXUeZq8v4N3F+by/NJ9X52+jc0woZw9K4dyhqfRPidG0DxERkXZACbRICwlyu5jYuxMTe3eiorqOT1ft5J3F+fx77maemr2J7ORIzh2SyjmDU5qcViIiIiKBz68JtDHmdOBhvEX5n7LW/umg9onAO8Am311vWmt/68+YRFpDeIibswencPbgFPaUVfPh8h28sziPhz5Zy0OfrGVwtzjOHZLCWYNSSI4OdTpcERERaQa/JdDGGDfwGHAqkAt8a4x511q78qBDZ1lrz/JXHCJOi48M4fsj0/n+yHTy91bw3pJ83l6cz2/eW8nv3l/J2B5JTBmSymn9O6uUnoiISBvgzxHoEcB6a+1GAGPMdGAKcHACLdJhpMSFc/2J3bn+xO6s3VnKu4vzeWdJHj/7zxLufzeIy0dncM3YLI1Ki4iIBDCXH8+dCmxrcDvXd9/BRhtjlhhjPjTG9PdjPCIBpVfnaH52Wm9m3jmJN24czcTeyTzx5QbGPfA5972znLy9FU6HKCIiIo3w5wh0Y+UG7EG3FwIZ1tp9xpgzgLeBnoecyJjrgOsA0tPTWzhMEWcZYxiekcDwjATu2L2PJ77cwEvfbOWlb7Zy3tBUbpjYne7JUU6HKSIiIj7+HIHOBbo1uJ0G5Dc8wFpbYq3d57v+ARBsjEk6+ETW2mnW2hxrbU5ycrIfQxZxVnZyFH++cDBf/nwSPxiVwXtL8znloS+5+aWFLM8rdjq8dsHjsWwtLGfBliIqa+qcDkdERNogf45Afwv0NMZkAXnApcD3Gx5gjOkC7LTWWmPMCLwJfaEfYxJpE1Ljwrn/nP7cclIPnv1qE8/P2cJ/l21nYu9kbp7UgxMyE5wOsX6jGWMgIiQwK2KWV9eyZkcpq7aXsmp7Cau2l7B6Ryn7qrzbuIe4XQzLiGNs9yTG9EhkUFocwW5/jiuIiEh7YKw9eFZFC57cOy3j73jL2D1jrf2DMeYGAGvtE8aYW4AbgVqgArjDWjvncOfMycmx8+fP91vMIoGouKKGF7/ewtOzN1FUVs2IzARumtSdE3slH/fmLJU1dewtr6G44rvL3vJqiitqKNl/u+LA9mLf8bUeizHQPyWGUVmJjMpO5ISsBGLDW7eaiLWW/OJKVuV7k+RVO0pYtb2UzYVl7O/iokOD6NM1mr5dY+jbNYaEyBDmby5izoZCVm4vwVqIDHEzIiuBsT2SGN09kb5dYnC18E6SxpgF1tqcFj1pAFOfLSJtWVN9tl8TaH9QZywdWUV1HdO/3cq0mRvZXlzJgNQYbp7Yg9P6d6lP9KpqfduP7ztwC/KGW48X+LYkL9xXRVl109MYjPEmnrERwcSFhxAbHkxseDAxvp+x4cFU1NQxb1MhC7fupbrWU59Qj/Ql1CMyE4iNaJmE2uOx7CytZFNBGZsLylm7s7R+VLm4oqb+uPSECPo2SJb7dY0hLT68yT829pRVM3djIXM2FDBnfSEbC8oASIgMYXR2IqO7JzK2RxKZiRHH/QeLEmgRkbZDCbRIO1Jd6+HtRXn888sNbCooIzUunGC3oXBfNaW+6QkHC3IZEqNCSIgMJSkqhITIEBIjQ0mMCiEu4ruEuGGiHB0WdNQjsJU1dSzetpevNxby9cYDE+p+XWMYlX10CXXDJHlLYTmbC8rYXOhNmLcUlVFZ46k/NjzY/d2ochfvz95doo+7nvb24grmrC/kK19CvaOkEoCusWGM6Z7EmO6JjO+ZRKeYsGafWwm0iEjboQRapB2q81g+XL6dtxflEx7iJjEyxHuJCiUhMuS7RDkqlJiwoOMePW2Oypo6lmzby9cbi/h6YyELtu6pT6j7dvEm1MMz4imprPElyI0nySFuF+mJEWQmRpCZGElmUqTvZwQpseEtPsXiYNZaNhWUMWeDd4R67oZC9pTX8JNTevKTU3o1+3xKoEVE2g4l0CLiqP0J9TebfAn1lj1U1XoT5RC3i24J4WQlRZLhS5KzfEly19hw3H5OkpvD47Gs2lFCfEQIKXHhzX68EmgRkbajqT47MJfOi0i7ExbsZmR2IiOzE7nt5J5U1daxZkdpfSIaSEny4bhchv4psU6HISIiDlICLSKOCA1yMygtzukwREREmk0FT0VEREREmkEJtIiIiIhIMyiBFhERERFpBiXQIiIiIiLNoARaRERERKQZlECLiIiIiDSDEmgRERERkWZQAi0iIiIi0gxKoEVEREREmkEJtIiIiIhIMyiBFhERERFpBiXQIiIiIiLNoARaRERERKQZlECLiIiIiDSDEmgRERERkWZQAi0iIiIi0gxKoEVEREREmkEJtIiIiIhIMwQ5HYCISFNq6zzU1FnqrKXOY7G+nwmRIRhjKC6vobSqBmu9x+//mZ4YAUDBvipKKmqw9W0WYwzdk6MAyN9bQUml9/HWgsUS5HLRu0s0AJsLyr5rB6y1hAa56ZcS06rvg4iIBBa/JtDGmNOBhwE38JS19k8HtRtf+xlAOXCVtXahP2MSacustdTUWVwGgtwu6jyWPeXV1NZZaj0e6jze9uSoUGIjgimrqmX1jhJq6mz9MbV1lv6pMXSNDWdXaSUz1xZQ5/FQ67F4PJZaj+WUvp3plhDBpoIy3luST53Hm7jWeix1Hg8/GJVBRmIki7ft5ZVvtlJT56HGY6mp9VDr8fCrs/qRkRjJxyt28M8ZG6j1eKiptb7jPLx87Si6JUTw7FebePDjNXgs1Fnv89dZyzf3nEynmDAe+Xw9j3y27pD3YcVvTiMyNIhHPl/H07M3HdK++U9nAvDX/63hlXnbDmiLDHGz4renA/DHD1fz3pL8A9o7RYcy7xenAPDb91fy+epdB7RnJ0Xy+c8mHvPvMJCpzxYROTp+S6CNMW7gMeBUIBf41hjzrrV2ZYPDJgM9fZeRwD99P0Uc5fFYXC4DwM6SSiqq66iq9VBVW0d1rYeY8GB6dfaOUr63JJ+yqlqqaj1U13qorvPQu3M0p/TrjLWWX769vP7+at8xJ/ftzPdHplNRXccF/5xDdZ2H2jqPL0G1XD02k+smdGdXaSUT/zKjPvn1+EZY7z2jD9dN6M7WonImPTjjkPh/f+4AfjAqg427y7jgn3MPaf/bJYM5b2gaG3eX8bP/LDmkPS0+gm4JEWzcvY+HPlkLgNtlvBdjOLlvZzISI9lZUsmMtbsIdrt8F0OQy0VVrQeAELeL6LAgQtwugtyGYLeLELeLkCDv7LE+XWK4dEQ6bpfBGHAb73OEh7gBGNcjifBgN24XuHxtLuM9D8BZg7rSu3M0GDCAMQaD9w8NYwwX53RjZFYixvurxBhDsO/3CnDN2EwmD+jieyyAITT4u5ltt57Ug++PSMcYb7vhu9jaG/XZIiJHz58j0COA9dbajQDGmOnAFKBhZzwFeN5aa4GvjTFxxpiu1trtfoxLjoG1FmvBYy0e38/QIBfGGCpr6qiu82A9+9u9xyRFeb9m31teTWllrXcU09r6c/X0JaDbisop2FdVf946j8VlDCOyEgBYvG0vO4or6pPL2jpLaLCLswalAPDxih1sKyqvb6+u9RAXEczVY7MAePjTdazfvY/qWl8SXOMhOzmSP5w3EIDLn/6GtTtLqa71+JJkD+N6JPHva0YAcO5jX7G9uPKA9+OMgV14/LLhAPzirWWUVNYe0H7h8DRO6dcZYwyfrNyJyxhCgryJY4jbRVmV93i3y5ASF0ZIkIsglzfJDHIZMhIjAYgMCeKykem4XS6CXKa+fXiG971JjArhd1P6e9t9bUFuF4NSYwHITIrg39eMINh3v9tlCHYb0hO8UxyGdItj5p2TCHKbAxLkyFBv1zCpdyfW/2GyL8E1HOy0/l04rX+XJj83k/p0YlKfTk22j+6eyOjuiU22j8hKqP8cNGZoejxD0+OPr73JVg772HZIfbaIyFHyZwKdCjT87jSXQ0cqGjsmFWjxznjCn7+gps6Dy3hHulzGcO7QVO44tRd1Hsvkh2f62gwuX/sFw1K5amwWpZU1XPnMvEPOOXVEOhfldGNXaSXXPb/gkPYfjsvi7MEpbC0s59ZXFtbPw7R4hxFvO6kn3+vfhdU7Srj91SVY3wTO/fM47z2zLyf2SmbBliLufH0pNJiHaYEHLhjEqOxEvly7m3vfXOZ7rK1/nicvH87gbnG8vzSf+99dUT+P0+NLYF+7fjS9u0Tz8jdb+f1/V9YnyL6ponx6x4mkJ0bw+Iz1/PmjNYe8vgW/PIXEqFD+8fk6HvtiwyHtq393OmHBbv7+6Tqem7P5gDa3y7Dh/84A4OHP1vH6gtwD2mPCglh6/2kATJu5gQ+W7TigPSU2rD6Bfumbrcxcu/uA9j5dousT6NU7Sli9o5TQIBehviTW1SAZHJQWS2pceH1yGxbsJjs5sr793jP6UlPnITTI7T1HsIvOMWH17e/fOp4gtzdBDm2QJO+3fzpAY0KCXDx15QlNtkeGBvGLM/s12R4TFszlozObbI8OC+bEXslNtocFu+vnCzfG5TK4ODRxlnYpoPpsEZFAZvYnbS1+YmMuAk6z1l7ru305MMJae2uDY/4L/NFaO9t3+zPg59baBQed6zrgOt/N3sCh2dyRJQEFx/A4fwvUuCBwYwvUuCBwYwvUuCBwY/NXXBnW2qb/qnGI+uxmCdTYAjUuCNzYFFfzBWpsrdpn+3MEOhfo1uB2GpB/DMdgrZ0GTDueYIwx8621OcdzDn8I1LggcGML1LggcGML1LggcGML1Lj8SH32UQrU2AI1Lgjc2BRX8wVqbK0dlz/rQH8L9DTGZBljQoBLgXcPOuZd4ArjNQoo1lw6ERFHqM8WETlKfhuBttbWGmNuAT7GWxLpGWvtCmPMDb72J4AP8JZDWo+3JNLV/opHRESapj5bROTo+bUOtLX2A7wdbsP7nmhw3QI3+zOGBo7r60Q/CtS4IHBjC9S4IHBjC9S4IHBjC9S4/EZ99lEL1NgCNS4I3NgUV/MFamytGpffFhGKiIiIiLRH/pwDLSIiIiLS7rS7BNoYc7oxZo0xZr0x5u5G2o0x5hFf+1JjzLBWiKmbMeYLY8wqY8wKY8yPGzlmojGm2Biz2Hf5tb/javDcm40xy3zPO7+Rdifes94N3ovFxpgSY8xPDjqm1d4zY8wzxphdxpjlDe5LMMZ8YoxZ5/vZ6K4bR/pM+iGuvxhjVvt+V28ZY+KaeOxhf+9+iu1+Y0xeg9/ZGU08trXfs1cbxLTZGLO4icf69T3riNRnH1N86rOPHI/67JaJTX12U2z9znBt/4J34csGIBsIAZYA/Q465gzgQ7w7/44CvmmFuLoCw3zXo4G1jcQ1EXjfofdtM5B0mPZWf88a+b3uwFuL0ZH3DJgADAOWN7jvz8Ddvut3Aw80EfthP5N+iOt7QJDv+gONxXU0v3c/xXY/8LOj+H236nt2UPtfgV878Z51tIv67GOOT332kWNQn90ysanPbuLS3kag67eitdZWA/u3om2ofitaa+3XQJwxpqs/g7LWbrfWLvRdLwVW4d29q61o9ffsICcDG6y1W1rxOQ9grZ0JFB109xTg377r/wbObeShR/OZbNG4rLX/s9bu31v8a7y1eltdE+/Z0Wj192w/Y4wBLgZeaannk8NSn+0f6rPVZzeb+uzmaW8JdFPbzDb3GL8xxmQCQ4FvGmkebYxZYoz50BjTv7ViwrvD9/+MMQuMdwexgzn6nuGtR9vUPw6n3jOAztZXA9f3s1Mjxzj93l2DdySqMUf6vfvLLb6vKp9p4itUJ9+z8cBOa+26Jtqdes/aK/XZx0Z99rFRn31s1Gc3or0l0KaR+w4uM3I0x/iFMSYKeAP4ibW25KDmhXi/7hoM/AN4uzVi8hlrrR0GTAZuNsZMOKjdyfcsBDgH+E8jzU6+Z0fLyffuF0At8FIThxzp9+4P/wS6A0OA7Xi/ejuYY+8ZMJXDj2Q48Z61Z+qzj436bP9Rn30g9dlNaG8JdIttRdvSjDHBeDvil6y1bx7cbq0tsdbu813/AAg2xiT5Oy7f8+X7fu4C3sL7dUxDjrxnPpOBhdbanQc3OPme+ezc/7Wo7+euRo5x6vN2JXAWcJn1TQQ72FH83luctXantbbOWusB/tXEczr1ngUB5wOvNnWME+9ZO6c++xiozz5m6rObSX1209pbAh2QW9H65ug8Dayy1j7UxDFdfMdhjBmB93dT6M+4fM8VaYyJ3n8d72KG5Qcd5uT2vU3+denUe9bAu8CVvutXAu80cszRfCZblDHmdOAu4BxrbXkTxxzN790fsTWch3leE8/Z6u+ZzynAamttbmONTr1n7Zz67ObHpj772KnPbn5s6rOb0tTqwrZ6wbv6eC3eFaG/8N13A3CD77oBHvO1LwNyWiGmcXi/zlgKLPZdzjgorluAFXhXr34NjGml9yvb95xLfM8fEO+Z73kj8HausQ3uc+Q9w/sfwnagBu9f2z8EEoHPgHW+nwm+Y1OADw73mfRzXOvxzkfb/1l74uC4mvq9t0JsL/g+Q0vxdrBdA+E9893/3P7PVoNjW/U964iXxn7XTvc/qM8+1tjUZx9bXOqzmxmX7/7ncLDP1k6EIiIiIiLN0N6mcIiIiIiI+JUSaBERERGRZlACLSIiIiLSDEqgRURERESaQQm0iIiIiEgzKIGWdskYU2eMWdzgcncLnjvTGKP6vyIiLUR9trQ1QU4HIOInFdbaIU4HISIiR0V9trQpGoGWDsUYs9kY84AxZp7v0sN3f4Yx5jNjzFLfz3Tf/Z2NMW8ZY5b4LmN8p3IbY/5ljFlhjPmfMSbcsRclItJOqc+WQKUEWtqr8IO+DrykQVuJtXYE8Cjwd999jwLPW2sHAS8Bj/jufwT40lo7GBiGdzcjgJ7AY9ba/sBe4AK/vhoRkfZNfba0KdqJUNolY8w+a21UI/dvBk6y1m40xgQDO6y1icaYArxblNb47t9urU0yxuwG0qy1VQ3OkQl8Yq3t6bt9FxBsrf19K7w0EZF2R322tDUagZaOyDZxvaljGlPV4HodWk8gIuIv6rMl4CiBlo7okgY/5/quzwEu9V2/DJjtu/4ZcCOAMcZtjIlprSBFRARQny0BSH+BSXsVboxZ3OD2R9ba/WWRQo0x3+D9A3Kq777bgGeMMXcCu4Grfff/GJhmjPkh3lGLG4Ht/g5eRKSDUZ8tbYrmQEuH4ptPl2OtLXA6FhEROTz12RKoNIVDRERERKQZNAItIiIiItIMGoEWEREREWkGJdAiIiIiIs2gBFpEREREpBmUQIuIiIiINIMSaBERERGRZlACLSIiIiLSDEqgRURERESaQQm0iIiIiEgzKIEWEREREWkGJdAiIiIiIs2gBFpEREREpBmUQIuIiIiINIMSaBERERGRZlACLSIiIiLSDH5LoI0xzxhjdhljljfRbowxjxhj1htjlhpjhvkrFhEROTL12yIiR8efI9DPAacfpn0y0NN3uQ74px9jERGRI3sO9dsiIkfktwTaWjsTKDrMIVOA563X10CcMaarv+IREZHDU78tInJ0nJwDnQpsa3A713efiIgEJvXbIiJAkIPPbRq5zzZ6oDHX4f26kMjIyOF9+vRp1hPtKq1iZ0klvTtHExKkdZMi4pwFCxYUWGuTnY7jGB1Vv328fbaISKBoqs92MoHOBbo1uJ0G5Dd2oLV2GjANICcnx86fP79ZT7S9uIIxf/qcq07qyR2n9jrGcEVEjp8xZovTMRyHo+q3j7fPFhEJFE312U4Ox74LXOFb1T0KKLbWbvfHE3WNDWdcjyTeWJCLx9PoILeIiBxZq/XbIiKBzJ9l7F4B5gK9jTG5xpgfGmNuMMbc4DvkA2AjsB74F3CTv2IBuHB4Gnl7K/hm0+HWx4iIdFyB1m+LiAQqv03hsNZOPUK7BW721/Mf7Hv9uhAbHszyvGJGd09sracVEWkzAq3fFhEJVE7OgW5V4SFu5tx9EpGhHeYliwScmpoacnNzqaysdDoUvwsLCyMtLY3g4GCnQwk4+hyISFvXobLJ/clzTZ2HYLeqcYi0ttzcXKKjo8nMzMSYxgo6tA/WWgoLC8nNzSUrK8vpcAKOPgci0tZ1uCzynjeXcvWz3zodhkiHVFlZSWJiYrtOmgCMMSQmJnaIEdZjoc+BiLR1HS6BTokNZ/b6AnL3lDsdikiH1N6Tpv06yus8Vh3l/ekor1Oko+lwCfR5w7ybZr25MM/hSESkNRUWFjJkyBCGDBlCly5dSE1Nrb9dXV192MfOnz+f2267rZUiFX/TZ0FEjleHmgMNkBYfwZjuibyxMJdbT+qh0QGRDiIxMZHFixcDcP/99xMVFcXPfvaz+vba2lqCghrvEnNycsjJyWmNMKUV6LMgIserw41Ag7cm9JbCcuZv2eN0KCLioKuuuoo77riDSZMmcddddzFv3jzGjBnD0KFDGTNmDGvWrAFgxowZnHXWWYA34brmmmuYOHEi2dnZPPLII06+BGkh+iyISHN0uBFogNMHdKGorJrspEinQxHpsH7z3gpW5pe06Dn7pcRw39n9m/WYtWvX8umnn+J2uykpKWHmzJkEBQXx6aefcu+99/LGG28c8pjVq1fzxRdfUFpaSu/evbnxxhtVpuwYBcrnAPRZEJGj1yET6IiQIK4dn+10GCISAC666CLcbjcAxcXFXHnllaxbtw5jDDU1NY0+5swzzyQ0NJTQ0FA6derEzp07SUtLa82wxQ/0WRCRo9UhE2iA2joP7y7Jp3NMGGN7JDkdjkiHcywjhP4QGfndN1G/+tWvmDRpEm+99RabN29m4sSJjT4mNDS0/rrb7aa2ttbfYbZbgfI5AH0WROToddgE2mUMD32ylqykSCXQIgJ4Rx1TU72Vep577jlngxFH6bMgIofTIRcRArhchvOHpTF7fQH5eyucDkdEAsDPf/5z7rnnHsaOHUtdXZ3T4YiD9FkQkcMx1lqnY2iWnJwcO3/+/BY515bCMk78ywzuPK03N0/q0SLnFJGmrVq1ir59+zodRqtp7PUaYxZYaztMHbTG+mx9DkSkrWiqz+6wI9AAGYmRjMhK4I0FubS1PyRERERExBkdOoEGuHBYGiFBLgrLDr/7lIiIiIgIdOBFhPtdODyNi0/o5nQYIiIiItJGdPgRaJfLu5V3WVUtVbVaKCIiIiIih9fhE2iA9btKOeEPn/Lxip1OhyIiIiIiAU4JNJCdFEVceDBvLMh1OhQRERERCXBKoPmuJvSsdbvZWVLpdDgi4icTJ07k448/PuC+v//979x0001NHt9SZTMlsOizICLHQwm0zwXD0/BYeGtRntOhiIifTJ06lenTpx9w3/Tp05k6dapDEYlT9FkQkeOhBNonKymS4RnxvK6a0CLt1oUXXsj7779PVVUVAJs3byY/P5+XX36ZnJwc+vfvz3333edwlNIa9FkQkePRIcrYfbhsO0Xl1Vw2MuOwx917Rl/Cgl0YY1opMpGO7ZIn5x5y31mDunL56Ewqquu46tl5h7RfODyNi3K6UVRWzY0vLjig7dXrRx/2+RITExkxYgQfffQRU6ZMYfr06VxyySXcc889JCQkUFdXx8knn8zSpUsZNGjQ8b04OWqt/TkAfRZE5Pi0+xFoay1vLsrjN++uZGV+yWGPHZ4RT/+U2FaKTESc0PCr+/1f2b/22msMGzaMoUOHsmLFClauXOlwlNIa9FkQkWPV7kegjTE8cMEgTvv7TH48fRHv3TqOsGB3k8ev2l7C83M3c/85/QkNavo4ETl+hxspDA9xH7Y9ITLkqEYaD3buuedyxx13sHDhQioqKoiPj+fBBx/k22+/JT4+nquuuorKSi0mbk1OfA5AnwUROXbtfgQavB3sXy8azLpd+/jjB6sOe+zu0ipembeNz1btaqXoRKQ1RUVFMXHiRK655hqmTp1KSUkJkZGRxMbGsnPnTj788EOnQ5RWos+CiByrDpFAA0zolcwPx2Xx77lb+GJ108nx2B5JdIkJ43XVhBZpt6ZOncqSJUu49NJLGTx4MEOHDqV///5cc801jB071unwpBXpsyAix6LdT+Fo6M7TevPV+gLufH0JH/54AsnRoYcc43YZzhuWyrSZG9ldWtXoMSLStp133nkHVNt57rnnGj1uxowZrROQOEafBRE5Fh0qgQ4LdvPI1KGc/Y/Z/Pz1JTxz1QmNVty4YFga/5yxgXcW53Ht+GwHIhUREZGW5vFYNheWsSyvmNLKWn4wylud67Ev1rN+174Djk2NC+dnp/UG4KH/rWHbnooD2rsnR3LLST0B+L8PVrG7tOqA9n5dY/jRBG8Ocd87yymprD2gfWh6HFeMzgTg7jeWUlXrOaB9VHYCl5yQDsDtry4+5LWc2CuZc4emUllTxz1vLjuk/Xv9OjN5YFeKK2q4/90Vh7SfMziFSX06saukkj9+uPqQ9ouGpzGmRxLbisp56JO1h7T/YFQ6wzMSWL+rlMe+2HBI+w/HZTEgNZblecU8PXvTIe03TexOz87RLNiyhxe/3nJI++2n9CI9MYI5Gwr4z/xDZwXcM7kPnWLC+GLNLt5dnH9I+/1n9yc2IpiPlu/gs1U7+ctFgw855nh0qAQaoFfnaO49oy/3vbuCF77eUv/hbahHpyhO6dsZVxspZ+fxWFwub6yfrdrJsrxithVVsLOkkjqPJS0+vP6Dc987y1m788BOomfnKH47ZQAAd72+lK1F5QB8r39nrhqTqbJ+IiLS5lhr6///emXeVt5dnM/yvGJKq7yJbJeYMC4bmY4xhrU7S1m0de8Bjy+trKm/vnJ7KWt3lh7QXuv57puLFfne/3cbCnF/N0t2aV4xhfuqD2iPDQ+uv754217Kq+sOaE+JC6u/vmDLnkNeX/fkSAA81jbaPiDVW1Wsps7TaPuIrAQAqmobb5/YOxmA8uq6RtvPGNgVgNLK2kbbLxiWBkBJRU2j7fv/oNhTVt1oe3mNt313aVWj7ZU13j84dpVUNtpeXedt315cwaJtew9pP16mrW0akpOTY493O1VrLdc89y1zNhTy3q3j6NU5uoWi84+GCfKcDQXM37yHbUXl5O6pIHdvOR4PfHX3SQD86Pn5fLpqJ52jw+gSG0aI20VaQjgPXTwEgF++vYy1Ow5NoP9w3kAAfvraErYVlVNSWcPqHaVcODyNP5w3QBVJpEWsWrWKvn37Oh1Gq2ns9RpjFlhrcxwKqdU11mfrcyAtzVrL9uJKluYWsyxvL0tzi1m1vZTZd00iLNjNQ/9bw4y1uxmYGsvgtDgGpsXSs1MUQe4OsxRMjlFTfXaHG4EGb2m7P184mNP/PpPbXlnEO7eMbTRBrPNYthaVk5UU2WqxWWvZWFDGnA2FzN1QwIr8EnYUV7Ls/tMICXLx8fId/HvuFjpFh9ItIYJh6fF0i4+o/0v7zxcMIiLU3WTC+/tzBx72+f968eD6OP7+6Toe/mwdZw3qysTenVr8tUrH1HBUqD1ra4MTrU2fg7ZvX1UtdXWW2Ihg9pZX80wjX9NP6tOJoenx7Cqt5MW5h35Nf9qALvRPiSVvbwWvztt6SPvZg1Po2TmaTQVlvLXw0K/xzx+WRmZSJC98vYVfv+OdphDkMvTqHM0pfTtRVlVLWLCbO77Xmzu+17sFXrWIV4dMoAGSo0P5y0WDuOa5+fzlozX88qx+hxzzq3eW89HyHXx9z8mEBPnvr9RtReUkRYUSHuLm6dmb+P1/vaX2UmLDGJoRzxkDu1JT5yEkyMUd3+vNPWf0bbKWdXxkSIvEZIzh9lN7ceagrvUj9OXVtUSEdNiPjLSAsLAwCgsLSUxMbNfJk7WWwsJCwsLCjnxwB6TPQdtT57Es3rbHO8KbW8zSvGI27N7H7af04raTe1JSUcs/vlh/yOOSokMZmh5PQWl1o+0ZiZH0T4ll+96KRtv7pcTSs3M0WwrLGm0/ISuBzKRIxnRP4rdT+jMwNZa+XWMOu9+DSEvokFM4Gvr1O8t5fu4WXvjhCMb3TD6g7YvVu7j6uW958vLhnNa/S4s9586SSuZuKGTOhgLmbixkW1EF/7oih1P7dWb9rn18u7mI0dmJZCRGBMx/LvM3F3H9Cwt46JIhnNgr+cgPEGlETU0Nubm5HWJzirCwMNLS0ggODj7gfk3h0Ocg0FXW1LF6RylLc/cSHRbEeUPTqK3zMOD+j6ms8dA5JpSBqXEMSotlYu9kBqXFOR2yyGFZa9lXVUt0WPP/HWoKRxPuPaMvczYU8tPXlvDRTyaQ0GAEd3zPJJKjQ3ljQe5xJdBFZdVU1tSREhfOht37OPmvXwLeBQSjshO4dlw2A1JjAO8Cxh6doo7vRflB17hwOsWEcfWz87jv7P5cOSbT6ZCkDQoODiYrK8vpMMRh+hw0bs76AnL3Vhxy//lDUwlyu1i0dQ/rDqoUYYCLcroB8O3mIjYVlB3QHuJ2ce7QVO/5NxSQe1AliYgQN2cNSgHgwY/X8MWaXazZUVq/QG58zyTOG5pGkNvF89eMJCMxgs4xbX9E3UlzNhRw53+WEh0WxMl9O3Fy384MSYurX+sUaDwey/wte3hncR4fLt9BeLCbUdmJjMpOYFR2It0SIpwO8QDWeqffLssr9l5yi1meV8yo7ESmXdFyYxcdPoEOC3bz8KVDOO+xOdz1xlKmXT68ftQ3yO3ivKGpPDVrIz+evoiHLx0KwGVPfX1IJzSmeyJ/PH8QAOc9/hVFZd7VtrV1lry9FVx6Qjf+dMEgspMiue/sfpyQmUDfrjG4A/QfzMFS48J5/YbR/Hj6Yu57dwXrd+3jvrP7aQGGiEgL+XbzHv726aHlws4ZnEKQG95ZnM9zczYf0OZ2mfoE+rVvt/GfgzYBiwkLqk+gX/x6Cx8s23FAe0psWH0Cvau0koTIEK6bkM2gtFgGpsWREvtdsry/aoMcG2stz3y1mf/7YBWZiRHEhgfzxJcbeeyLDSRFhTCptzeZHt8zichQZ9Mzay0rt5fw7uJ83luST35xJWHBLk7u2xmPx/L56p284ZuTnhoXzqjsREZ39ybVafGtl1AfnCwv9yXM+yt8hLhd9OkazVmDUxjTPbFFn7vDT+HY76lZG/n9f1fxx/MHMnVEev3924sreOh/a8lIjKiv9/iH/648pN5j/5TY+nqPv35nOSUV35W/6dEpiom9O9WXlGnL6jyWP3+0midnbuSBCwbW16gUkaOjKRzSUFFZNbl7yhmUFkdtnYf8vZW4DhqXSI0LxxjD3vJq9lXVHnKO/QlLUVk15dUHtruMISUuHIDCfVVU1BxYKs3tMnSNDW/BVySN2V+r+a1FeZzWvzN/vXgIUaFBFJfXMGPtLj5dtYsZa3ZRWllLiNvF6O6JnNK3Eyf17UxqXOv9fjYXlPHuknzeXZLP+l37CHIZJvRKZsqQFE7p27k+sfd4LOt27ePrjYX1lz3l3rwnLT7cN0Ldsgm1tZZtRRUszdtbnywvzyuh2Jdvhbhd9O4SzcC0WAamei+9Okcf9xq2pvpsJdA+Ho/limfmsWDLHt6/bRzdkwNvGkUgmbVuN2O7J+FymQPK7InI4SmBlv1W5pdw3QvzqfNYZtw5UeVC26m8vRVc/8J8VuSXcMcpvbh5Uo9G/8+sqfPw7eYiPl+1i89W76qfjtOnSzSn9O3MyX07MdgPUz12lVTy/tLtvLMknyW+eskjshI4Z3AKZwzsesDU1qbsT6jnbijg641FfLOp8YS6T5doKmvq2FdVS1lVHWVVteyrqqW8upZ9vtv77yur/u6YsqpaiitqKPPVyg52G/p0iWFAaqz325IWSpYbowT6KOwsqeS0v8+kW3wEb9w4xq+VN9qLvL0VXPXMPH537gBGZbfs1yMi7ZESaAH4cNl27nhtCTHhQUy7PIfB3eKcDkn8YO6GQm5+eSE1tR4enjqEk/p0PurHbti9j89W7eTTVbuYv7kIj4WkqFBO6pPM8Ix4okKDiQx1ExUaRGRoEFGhQUSEuIkMDSI0yHXYIgTFFTV8vHwH7yzJY+6GQjzWu3PilCEpnD04pf5bi2Pl8VjW7irl6w2FhyTUTQlxu4gMdde/lsj61+UmIiSI6LAgenaK9ibLXaJa7Q9OJdBH6aPlO7jhxQXcOLE7d53e57jPt79OZmSou13OF95aWM7Vz81ja1E5fzh3IBef0M3pkEQCmhLojs3jsfz907U88vl6hqXH8cQPhtNJi/LaHWstz83ZzO//u4qspEimXT6c7OP4ZntveTUz1uzm01U7+XLtbkorD53K01CQyzRIRL0J6P7r1bUevlpfSHWdh4zECKYMTuGcISn06OS/TeX2J9SbC8qICAk6IDZv4h8UsIOWjlThMMacDjwMuIGnrLV/Oqg9FngRSPfF8qC19ll/xnQkpw/owtQR3Xjiyw1M6JnM6GZOOq+p87Bo615mr9vNzHUFLM3dy/7dPsOCXUSFBhPV4C+sqNAgosKCDrgdGRpEtO9nVlIk/VJi/PBKW0Z6YgRv3jSWW15eyM/fWMr63fu46/Q+bWZxpIh8py322W2NMbBu1z4uzknjd+dql9f2qLKmjnvfWsabC/P4Xr/O/PXiwcdUPq2huIgQzh2ayrlDU6mp87CjuNI3xeG7qQ/7qmopr6qlrHr/FIn999VRVu29vqu0kjqP5QejMpgyJIVBabGtUi7X5fJOuejTJXDzmeby2wi0McYNrAVOBXKBb4Gp1tqVDY65F4i11t5ljEkG1gBdrLXVjZ0TWmc0o7y6lrMemU1FTR0f/XgCsRFNf/CttWwuLGfWut3MXFvA1xsL2VdVi8vAkG5xjOuRRGxECPsqvfN5Siu/+1A3/ICXVXnbqmo9hzzHiKwErhufzUl9OgXsXOPaOg+/fX8lz8/dwp2n9ebmST2cDkkkIAXqCHRb7rPbgi2FZbiMoVtCBNW1HoLdJmDq/EvLydtbwQ0vLGBZXjG3n9KLW09qfL6ztB1OjECPANZbazf6ApgOTAFWNjjGAtHG24tEAUXA4b+XaAURIUH8/dIhnP/4HO59axmPfn/oAR1dcXkNX20oYNa63cxa911dzbT4cM4ZksKEnkmM7p5EbHjz/+KsqfMckGDPXlfAM7M3ce3z8+nRKYofjc9iypDUgNtlKcjt4rdTBjA0PY5T+7XcpjMi0mrabJ8d6GavK+DmlxfSt2s0068bHbBfVcvx+XpjITe/tJDqWg9PXZHDKf2Ofr6ztD3+TKBTgW0NbucCIw865lHgXSAfiAYusdYeOgTrgEFpcdzxvV78+aM1jP82ie6dopi19sBpGdGhQYzunsj1J3ZnfI+kFtk5MNjtIi4ihLgI76rXPl1iuHJMJh8s286TX27krjeW8ZeP13L12Ex+MDLjsKPjTjhvaBrgXaDwwtzN3DRRf32LtBFtus8ORPvr/v7hvyvp0SmKBy4Y5HRI4gfWWv49ZzO/+6+3vvO0K3JUyasD8GcC3VjWdPB8kdOAxcBJQHfgE2PMLGttyQEnMuY64DqA9PTWqzt8/YTufLlmN3e/uQygflrGLSf1ZELPJAZ3iyO4FRYGBrtdTBmSyjmDU/hqfSFPztzAXz5ew2NfrOeSE7pxzdisgNsJ6JOVO3nwf2upqvXw0+/1djocETmyNt9nB5LKmjp+8dZy3liYy/f6deahS7x1f6V9afh7PqVvZ/52yfHPd5a2wZ//mnOBhiUZ0vCOWjR0NfAn652Ivd4YswnoA8xreJC1dhowDbzz6fwW8UHcLsMjU4fy7FebGdIt9pinZbQUYwzjeiYxrmcSK/NLeGrWRl6Yu4Xn527hjIFduX5CdsBs1nLBsFS+3VTEPz5fT58uMZw5qKvTIYnI4bX5PjuQeKxl1fYSfnxyT358ck99E9cO5e+t4IYXF7A0t5ifnNKT207S77kj8eciwiC8C1JOBvLwLkj5vrV2RYNj/gnstNbeb4zpDCwEBltrC5o6rxakHCh/bwXPfrWJV+ZtY19VLWO6J/KjCdlM7JXs+AKVqto6pk77mlXbS3njxjEBXU1EpLUE8CJC9dktYHleMVlJkUSGBlFZUxdw61WkZXyxZhd3/mcJlTUe/nbJEE7VfOd2q6k+22/zD6y1tcAtwMfAKuA1a+0KY8wNxpgbfIf9DhhjjFkGfAbcdbiOWA6VEhfOL87sx5x7TuKeyX3YsHsfVz/7Laf/fRZvLMjF43Fu8Cc0yM0TPxhObHgwv3pnOW2t5rhIR6I++/h9tb6A8/85hz9/tBpAyXM7tKesmjteXczVz35LQmQIb988VslzB6WNVNqZ6loP7y7J518zN7JmZym3ndyTO07t5WhMK/NLSIwKobM2CxAJ2BFof+koffaesmpO+/tMYsKD+c/1o4k/iu2Ppe2w1vLBsh3c9+5y9pbXcNOkHtw8qbvqeHcAjmykIq0vJMjFhcPTuGBYKne+vpRHPlvHwNRYR/9C3j91o85j+XTVTk7rrzJ3ItJ+WGu5961l7Cmv5pmrTlDy3M7sKqnkV+8s5+MVOxmYGssLPxxJ366aktjRqRhlO2WM4ffnDmBgaix3vLqYDbv3OR0Sr83fxvUvLODVb7c6HYqISIt5Y2EeHy7fwR2n9g6Yhdxy/Ky1vDZ/G6c89CUz1uzmnsl9eOumMUqeBVAC3a6FBbt54vLhBAe5uP6FBeyrcna/g4uGpzG+ZxK/fHs5C7YUORqLiEhLGdItjstHZXDdhGynQ5EWsq2onCuemcfPX19Kny4xfPjj8Vx/YneCWqF0rbQN+iS0c6lx4Tw6dSgbd+/jzv8scXQhX5DbxaNTh5ESF871Lyxke3GFY7GIiBwvj8diraVHpyh+d+4A3Cph1uZ5PJbnvtrEaX+fycIte/jduQOYft0osrUxihxECXQHMKZHEvdM7suHy3fwzy83OBpLbEQw/7oih4rqWn78ymJV5hCRNuvxGeu59ZVFVNdqM8b2YP2ufVz85Fzuf28lJ2Qm8L87TuTyURmq7SyN0iLCDuLa8VkszSvmwY/XMCAllgm9kh2LpVfnaB79/jASIkMcr1UtInIslubu5e+fruP0AV0Idqsfa8tq6jxMm7mRhz9bR0SIm4cuHsx5Q1P1/5MclhLoDsIYwwMXDGTdzlJufWUR7986ztHtvyf16VR/ff2uffTopK/HRKRtKK+u5SfTF5McHcofzh2oRKsNW55XzF1vLGVFfglnDOzCb84ZQHJ0qNNhSRugKRwdSERIEE9ePhxrLde9sICK6jqnQ+KtRbl8729fMnPtbqdDERE5Kv/3wSo2FpTx14sGExsR7HQ40gyVNXUs3raXF77ewh2vLWbKY1+xq7SKJ34wjMcvG67kWY6aRqA7mIzESB6eOpRrnvuWe95cyt8uGeLo6Mn3+nWhV+eN3PLyQt69ZRyZSZGOxSIiciRFZdX8d+l2fjQ+izE9kpwOp02rrKnjzx+t4X8rd5AWH052chTZSZFkJ0eSlRRFt/jw46p6UVlTx5odpSzNK2Z5bjHL8opZu7OUWt8OvfERwVyck8bdp/fVH0LSbEqgO6BJvTtxxym9+OsnaxncLY6rx2Y5FktkaBD/uiKHsx+dzY+en89bN48lKlQfSxEJTAmRIXz0kwnEhivhOh7L84q5/dXFrNu1j5P6dGJveTUfLNvO3vKa+mOCXIb0xAiyk6LITo4kOymSrKRIspOjSIo6cA1NVW0dq7eXsiyvmOV5xSzNPTRZHpAay/V9shmYGsuA1FhS48I1/UaOmTKVDurmST1YklvM7/+7in5dYxiZnehYLN0SInj8+8O4/Jl53P7qYqZdPlydmogEFGstHy3fwWn9u9A5JszpcNqsOo9l2syNPPTJGhIiQ3jhhyMY3/O7Re17yqrZWFDGxt372FRQxsbdZWwqKGPmut0HVDuJDgsiOymS1PhwthSWs3ZnKTV13mQ5LiKYgamxXNfbmywPTFOyLC1PCXQH5XIZHrpkMOc++hU3v7yQ924dR9fYcMfiGdMjifvP7kdUWJA6OREJOK9+u42731zGI1OHcs7gFKfDaZO2FZXz09eWMG9zEWcM7ML/nTeQuIgDtz2PjwxheGQIwzPiD7i/zmPJ31vBxoIyNu3e5/1ZUMbK/BK6JURw7fhsBvlGltPilSyL/ymB7sBiwoKZdsVwpjz6FTe+uJBXrx9FaJDbsXguH53p2HOLiDRlU0EZv3lvJWN7JHLWwK5Oh9PmWGt5a1Ee972zAgv89aLBnD+seWXi3C5Dt4QIuiVEcKKDZVhF9lMVjg6uR6do/nrxYBZv28v9765wOhwA/vq/Nfz907VOhyEiQk2dh5+8upiQIBcPXjRYm2o0097yam55ZRF3vLaEPl2j+fDH47lgeJpGiKXNUwItnD6gKzdN7M4r87bxyrytTofD+l37mD5vm3YpFBHHPfr5epZs28sfzhvg6DS3tmj2ugJO//ssPl6+g5+f3pvp1412dP8BkZakKRwCwE+/15vl+SXc984K+nSJZmh6/JEf5CcTeyfz4fIdrN5RSt+uMY7FISIytkcSVbUezhqkec9Hq7Kmjr98vIanZ2+ie3IkT105lgGpsU6HJdKiNAItgHd+2SOXDqFzbCg3vriQ3aVVjsVyYi/vLoUz1mhzFRFxxv5vwEZkJXD35D4OR9N2rMwvYcqjX/H07E1cOTqD928dr+RZ2iUl0FIvLiKEJ3+Qw96Kam5+eSE1dZ4jP8gPusSG0bdrDDPW7HLk+UVE7n1rGX/8YJWmkh0lj8cybeYGzn3sK4rKq3nu6hP4zZQBhIc4tzBdxJ+UQMsB+qXE8KfzBzFvUxGPfr7esTimDEkhPSFC/3mJSKv7eMUOXpm3DZfLaLHbEdTUeVi/q5TvP/U1//fBak7q04mPfzKBib07OR2aiF9pDrQc4tyhqbyxMJf3luRz+6m9HInhhhO7O/K8ItKx7Sqt5J43l9E/JYbbT3Gm/wsENXUedpdWsau0ip0llewqqfzuemkVO0uq2FVSSWFZNQCRIW7+fOEgLlKFDekglEBLoyb17sRv31/JtqJyx1ZNW2spLKsmKSrUkecXkY6lsqaO215ZRFlVLQ9fOoSQoPb/Ja21lgVb9vDmojzy91aws6SK3aXexPjgLwBdBpKjQ+kUHUZqXBhD0+Po5Ls9oVcSafGqsCEdhxJoadSEXkkAzFpXwPdHpjsSw6/eWc4nK3fy9T0na0RDRPxuRX4Ji7ft5YELBtGjU7TT4fhVncfy8Yod/GvWRhZt3UtUaBBZSZGkxoUxpFscnWO8iXHnmFA6x4TRKTqUxKhQ3KqDLQIogZYmdE+OIiU2jJlrdzuWQA9Oi+PFr7eyansp/VJUzk5E/MNaizGG4RnxzPz5JDpFhzkdkt+UVdXyn/nbeOarzWwtKicjMYLfTunPhcPTiAhRSiBytPSvRRpljGF8z2Q+WL6d2joPQe7W/yrzxN7e7Vq/WLNLCbSI+EV1rYebX17Iaf27cOHwtHabPO8qqeS5OZt56ZutFFfUMDwjnnvP6MOp/bpoVFnkGCiBliZN6JXMq/O3sSR3L8MzElr9+TtFh9E/JYYv1+zm5kk9Wv35RaR9q6nzcMvLC/lk5U4m9Ep2Ohy/WL2jhKdmbeKdxXnUeSyn9e/CteOzGZ7h3GZZIu2BEmhp0tgeibgMfLm2wJEEGryLGf/55QaKK2qIDQ92JAYRaX9q6jzc9soi/rdyJ/ef3Y/LR2U4HVKLsdYye30B02ZuZNa6AsKD3Xx/RDrXjMsiIzHS6fBE2gUl0NKkuIgQBqXFMXPtbu5wqJzdecNS6ds1htAOsBpeRFqHx2O5/dXFfLh8B788sy9Xjc1yOqQWUV3r4d0l+Tw1ayOrd5SSHB3Knaf15rKR6cRFhDgdnki7ogRaDmtCr2Qe/Xwde8urHemAuydH0T05qtWfV0TaL5fL0LNTNPdMjuXa8dlOh3PcistreGneFv49ZzM7S6ro3Tmav1w4iHOGpBAapJ0ARfxBCbQc1om9knjks3V8tb6QMwd1dSSGbUXl/G/lTq4ek4lLi11E5BjVeSz5eyvolhDBj0/p6XQ4x21bUTlPz97Ea/O3UV5dx7geSfz5wsFM6Jmk0p8ifqYEWg5rcFoc0WFBzFy727EE+ptNRfzu/ZWMzEpgQGqsIzGISNvm8VjueXMpH6/YySe3T6BTTNuttrFo6x6emrWJD5dvx2UM5wxJ4dpx2apWJNKKlEDLYQW5XYztnsSsdbvra6W2thN9q+NnrNmlBFpEms3jsfzi7eW8Nj+X207u2SaT5zqP5ZOVO3lq1kbmb9lDdFgQ103ozlVjMukS2/Zej0hbpwRajmhCr2Q+WrGDDbv3ObI7V3J0KANTY5mxZje3nNT2v3YVkdZjreW+d1fwyryt3DypO7e3sakbFdV1vL5gG0/P3sTmwnLS4sP59Vn9uPiEbkSF6r9wEafoX58c0fie3m29v1xb4Nj2tpN6J/PoF+sdW8woIm3TK/O28cLXW7h+QjY/+17vNjM3eHdpFc/P3cyLX29hT3kNg7vF8dhpfTitf2dHNrYSkQMpgZYj6pYQQXZSJLPW7eaH45wp93Ri7048PmMDK/JLGNsjyZEYRKTtOX9YKkEuw0U5aW0ieV67s5SnZm3k7UX51Hg8nNq3Mz+akE1ORnybiF+ko1ACLUdlQq9kpn+7lcqaOsKCW78s0pBucSz69alEh2kzFRE5PGstz8/dwjmDU4iPDOHiE7o5HdIRVdXWcfuri/lg2Q7Cgl1cfEIaPxyXTVaSNj4RCURKoOWojO+ZxHNzNjN/8x7G9Wz9EWC3yyh5FpGj8rdP1vLI5+spq67lpok9nA7niKy13PfOCj5YtoNbT+rB1WOzSIjUVDWRQKaJVHJURmUnEuw2zFq327EY1u4s5aIn5rAst9ixGEQksO0oruSRz9dz/rBUbpjQ3elwjsrL87Yy/dtt3DypOz/9Xm8lzyJtgBJoOSqRoUHkZCTw5VrnEujEyBDmb9nD56t3ORaDiAS2bXvKATh3SGqb2Hhp/uYi7n93BRN7J3PHqb2dDkdEjpISaDlq43slsXpHKbtKKh15/sSoUAalxTFjrRJoEWlc/t4KALq2gdrIO0squfGlhaTEhfPwJUNxt4GEX0S8lEDLUZvQ07uhyax1BY7FMLFXMou37aWorNqxGEQkcEWHBTE6O5GuceFOh3JYVbV13PDiAsqqapl2eQ6xEVrjIdKWKIGWo9avawxJUSHMdHAe9MTeyViLo3OxRSRwndSnM69cNyrgNxm5/92VLNq6lwcvGkzvLs7U1xeRY+fXBNoYc7oxZo0xZr0x5u4mjplojFlsjFlhjPnSn/HI8XG5DON6JDF7XQEej3UkhkFpcUwe0IXYcI3WiLQ09dmt4+VvtvLKvK3cNLE7Zwzs6nQ4InIM/JZAG2PcwGPAZKAfMNUY0++gY+KAx4FzrLX9gYv8FY+0jAm9kiksq2bl9hJHnt/tMvzzB8OZ2LuTI88v0l61lz77kifnctfrS50Oo0kLtuzhvneXc2KvZH76PS0aFGmr/DkCPQJYb63daK2tBqYDUw465vvw/+3deXycZb3//9cnM9nXZumaNt3ShkIXIECBLuxQRAFl86tHlE04Lng8ckA9HvG4/FRcOHpQREVQPCK7LGVTKqWyptCVrnRL26RNm2bfM9fvj5nWUJI2aXPPPZN5Px+P+zEz99xzX5/emVz95L6v+3PxmHNuG4BzTneHxbg5B6b19ncIRW1zB3ub2n2NQWSIGRJ99ns1zcTqhH27Gtq46YGljMpN52dX6aZBkXjmZQI9Bqjs8Xp7ZF1PU4BhZvZ3M1tqZp/yMB4ZBMOz0zhmVI6vY5Ab2zo5+bt/5Q+vb/UtBpEhKO777PaubvY0tTMyBitwdHSFuOmBpTS1d3HPp07UTYMicc7LBLq3P60PHjgbBE4EPgScD3zDzKZ8YEdmN5hZhZlV1NTo5jG/zZtSyNKt+2hu7/Kl/ey0ZKYX57Jonb4LIoMo7vvsXfXhq1Kjc2OvAsftT63m7W113HHZTMpG5vgdjogcJS8T6O3A2B6vi4GdvWzznHOu2Tm3B1gMzDx4R865e5xz5c658qKiIs8Clv6ZX1pEZ7fjtff2+hbDGVOGs2J7nYZxiAyeuO+zq+ojNaDzYusM9J/e3Mb/vbGNG+dP4kMzdNOgyFDgZQL9FlBqZhPMLAW4CnjyoG3+Asw1s6CZZQCnAGs8jEkGwYnjh5GeHPB1GMeZZeFydn6W1BMZYuK+z85OS+ajx49hfEGm36Ec8Pa2fXzzL6uZW1rILefrpkGRocKzQpnOuS4z+zzwPBAA7nXOrTazGyPv3+2cW2NmzwErgBDwG+fcKq9iksGRGgwwe2I+i32cUOW40bkUZqXw93U1XHp8sW9xiAwVQ6HPnjY6h59cOcvvMA7Y3dDGjX9YysjcNH7+cd00KDKUeFpp3jm3EFh40Lq7D3p9B3CHl3HI4Js3pYhFT71LZW0LY/Mzot5+UpLx0ytnMc6HtkWGqnjvs9s6u0kNJmExUIajoyvEv/7xbRrburj/mpPJy0jxOyQRGUT9GsJhZqeb2Ytmtt7MNpnZZjPb5HVwErvmRqb19nMIxdzSIkpi6FKtiPjrC396h0vu+offYQDw30+vpmLrPu64fAbHjNJNgyJDTX/HQP8W+AkwBzgJKI88SoKaVJTJmLx0FvtcD/qJd3awcGWVrzGISGyoqm9lWKb/Z3r//NY2Hnh9G5+dP5GLZoz2OxwR8UB/h3DUO+ee9TQSiStmxrwphTy9vIrO7hDJAU9nhe/TH17fSmd3SNPhighVdW1MH5PrawzvbNvHN54I3zT4H+eX+RqLiHinv1nPIjO7w8xONbMT9i+eRiYxb25pEY3tXSyvrPMthjOmFLFiez01jSpnJ5LI2jq72dvcwSgfa0BX17dx0wNvMyI3VTcNigxx/U2gTyE8bON7wI8jy4+8Ckriw+mTCkkyfB3GcWbZcMDfGETEf7sa2gAY5dMshO/VNPGxX75KQ1snv/pkuW4aFBni+pVAO+fO7GU5y+vgJLblZiQzc2weL/tYzm7aqBwKs1L5uxJokYSWnhzgc2dOYkZxXtTbfmfbPi775au0dXbz4A2zmTZaNw2KDHX9rcKRa2Y/2T81q5n92Mz8HWgmMWFeaRErttdR19LhS/tJScYZU4uojsxAJiKJaXhOGrecX8bUkdlRbXfRut38v1+/QXZaMo/edJovCbyIRF9/h3DcCzQCV0SWBuB3XgUl8WPelPCMgEs2+ncW+nuXTufhG0/zrX2RWGFmjWbW0MvSaGYNfsfnpdrmDupbOqPa5iNLt3Pd/RVMLMrkkZtOZXyhymqKJIr+JtCTnHPfdM5tiizfAiZ6GZjEh5nFuWSnBX0dg5wSDH+NnXO+xSASC5xz2c65nF6WbOfckB5X8NMX1zPvjkVRacs5xy///h5feXg5syfm8+ANsxme7c/YaxHxR3/L2LWa2Rzn3BIIT6wC6Jq5EAwkMWdyIa9s2INzzrcZwH764nre2LyXB2841Zf2RWKBmeUf6n3nXG20Yom2qvrWqNxAGAo5vv3Mu/zuH1v48MzR/OjyGaQGA563KyKxpb8J9E3A/ZFxzwbUAp/2KiiJL/OmFPHsqmo27m6idER0xx/ulxJM4vVNtexuaGN4js4EScJaCjjC/fTBHEP4ymFVfZvnCXR7Vzf//tBynl5RxTWnT+A/P3QMSSpVJ5KQ+luFY5lzbiYwA5junDveObfc29AkXswtLQRgsY/VOM6YGp5aXNU4JJE55yY45yZGHg9ehmzyDJEEOs+7GtCNbZ1cc99bPL2iitsWlPGNi5Q8iySyQ56BNrNPOuceMLMvH7QeAOfcTzyMTeJE8bAMJhZlsnh9DdfOmeBLDNNG5TA8O5WX19VwRflYX2IQiSVmNgwoBQ6clnXOLfYvIu+0dXZT29zBKI+uPu1ubOMzv3uLtdWN/PjymXzsxGJP2hGR+HG4IRz7byn257q8xI15pUU8+NY22jq7SUuO/nhAs3A5u2dXVdPVHSLo09TiIrHAzK4DbgaKgWXAbOA1YEjW73cOvnPJccwamzfo+968p5lP3fsGexo7+M3V5Zw5dfigtyEi8eeQCbRz7leRx29FJxyJV/OmFHLfq1uo2LKPOZEhHdF28awx5Gem0trZTbYSaElsNwMnAa875840szJgyPbj6SkBPjm7ZND3u2J7HZ/53Vs44E83zPYkQReR+NTfiVR+aGY5ZpZsZn8zsz1m9kmvg5P4MXtiASmBJBZv8G8M8umTC7ltQRnZacm+xSASI9qcc20AZpbqnFsLTPU5Js9U17expqqB7tDglbJcvL6Gq+55nfSUAI/ceKqSZxF5n/6epjvPOdcAXARsB6YAt3gWlcSdjJQg5eOH+VoPGsJ3yb+9bZ+vMYjEgO1mlgc8AbxoZn8BdvoakYcefXs7C/7nFTq6QoOyvyfe2cE1971FSUEmj910GhOLsgZlvyIydPQ3gd5/Su9C4E9DuZaoHLm5pUWsrW5kd0ObbzH8dslmPvqLV9nd6F8MIn5zzl3qnKtzzt0OfAP4LXCJr0F5qKq+lbyMZNJTjv7+i/97Yxtf+vMyyscP48+fna2ymCLSq/4m0E+Z2VqgHPibmRUBylDkfeZN8b+c3bzScDm7V9b7F4OI38xstpllAzjnXgYWAcf7G5V3quraGJV79CXsOrpC3PH8Wk6dWMD915xMjoaDiUgf+lsH+jbgVKDcOdcJNAMXexmYxJ9jRuZQmJXq6zCOaaNyKMhM8XUstkgM+CXQ1ON1c2TdkFRV38boQZhE5a9rdrGvpZPPzp+o2QVF5JAOVwf6LOfcS2b20R7rem7ymFeBSfxJSjLmlhby8voaQiHnyyQD+2NYvGGPbzGIxABzzh24o845FzKz/s48G3eq6ls5flzeUe/noYpKRuWmMTdyJUtEpC+HOwM9P/L44V6WizyMS+LUvCmF1DZ3sHpng48xFPkeg4jPNpnZFyOVk5LN7GZgk99BeeXHV8zk4yePO6p9VNW3snh9DZedWExAf3iLyGEcrg70NyOPn4lOOBLv5kwOn7lZvKGG6cW5vsRwdtkIHr3pNI4Zpfl/JGHdCPwM+E/AAX8DbvA1Ig+dVTbiqPfx6NLthBxcplkGRaQf+lsH+nuRkkj7Xw8zs+94FpXEraLsVKaNyvF1HHRuRjInlgzTbISSsJxzu51zVznnhjvnRjjn/p9zbrffcXmhur6Nl9buoqm964j3EQo5HqrYzuyJ+ZQUZB7+AyKS8PqbYSxwztXtf+Gc20e4pJ3IB8yfWsTSrft8LSW3cXcjtz+5msa2Tt9iEPGLmU2JTHq1KvJ6hpn9p99xeWHJxj1cc18Fexrbj3gfb26pZVttC1eeNHYQIxORoay/CXTAzFL3vzCzdCD1ENtLAruifCzdznH/q1t8i6GmsYP7Xt3Ca+/t9S0GER/9Gvgq0AngnFsBXOVrRB6prm8FYORRVOF46K1KslODXHDsqMEKS0SGuP4m0A8Qrv98rZldA7wI3O9dWBLPJhRmcv60kTzw+jaaj+Ky6tE4sWQYmSkBlbOTRJXhnHvzoHX+/DJ6bGd9G/mZKaQlH1nZuYa2ThauquLDs0YPykQsIpIY+lsH+ofAd4BjgGOBb0fWifTq+nkTqW/t5KGKSl/aTwkmceqkAl5eX0OPal4iiWKPmU0ifAMhZnYZUOVvSN6orm9j5FHMFvj08iraOkNcWa7hGyLSfwO5y2oN8Jxz7t+BV/bPciXSmxNLhlFeMozfLtlMV3fIlxjmTSmisraVLXtbfGlfxEefA34FlJnZDuBLhCtzDDlV9W2MzjvyBPrPFZVMHZHNDJ+qBolIfOpvFY7rgUcId8gAY4AnPIpJhojr501k+75Wnl1V7Uv786cUkZ+ZQmWtEmhJLM65Tc65c4AioAw4A5jja1Ae+Z+rZvEfF5Qd0WfXVTeyvLKOy8uLD54kTETkkPp7BvpzwOlAA4BzbgMw3KugZGg495gRTCzM5J7Fm3wZRlFSkEnF189h3hTNKiaJwcxyzOyrZva/ZnYu0AJcDWwErvA3Om9MGZHNlBFHdkH04YpKkgPGpcePGeSoRGSo628C3e6c69j/IjIlrAaWyiElJRnXzZ3Iyh31vL6p1rcYnHMaBy2J4g/AVGAlcD3wAnA5cIlz7mI/A/PCnqZ2Hnh9K1WRShwD0dEV4rF3dnDOMSMoyFJRKREZmP4m0C+b2deA9MhZjYeBp7wLS4aKj54whoLMFO5Z/J4v7a+pamDuDxfx2iaVs5OEMNE592nn3K+AjwPlwEXOuWX+huWNtVWN/OcTq9iyZ+DDtF5au4va5g6u0M2DInIE+ptA3wrUED6r8VlgIeEpYkUOKS05wNWnjWfRuhrW72qMevtj8zOorm9j8fo9UW9bxAcHZg5yznUDm51z0f/Fi5L9Z56P5CbChyq2MzInTUO8ROSIHDaBNrMkYKVz7tfOucudc5dFnuuauPTLJ2eXkJacxK8Xb4p621mpQU4sGebr1OIiUTTTzBoiSyMwY/9zM2vwO7jBVl0fnu10xADL2FXXt/H3dbv52IljCCTp5kERGbjDJtDOuRCw3MzGRSEeGYLyM1O4onwsTyzbwe6G6E/vPW9KEe9WNfg6tbhINDjnAs65nMiS7ZwL9nie43d8g21nfRsFRzCJyqNvbyfk4PITNXxDRI5Mf4dwjAJWm9nfzOzJ/YuXgcnQcu2cCXSHHL/zYXrv+ZFLtK9oGIfIkFJd38qoAQ7fcM7xcEUlp0zIZ3xhpkeRichQF+zndt/yNAoZ8koKMrnguJE88PpWPnfmZLJS+/vVO3rTRuVw9akl+s9SZIi588rjaWjrPPyGPby5uZYte1v4wlmlHkUlIongkFmMmaURnr1qMuEbCH/rnOuKRmAy9Fw/dyILV1bz57cquXbOhKi1m5RkfOvi46LWnohER25GMrkZyQP6zEMV28lKDbJg+kiPohKRRHC4IRz3Ey6DtBJYAPzY84hkyDp+3DBOHp/PvUs20xnl6b2dc6ytbmCXD2OwRWTwtXZ0c8fza1m1o77fn2ls62Thyio+PHM0GSnRuwomIkPP4RLoac65T0Zqil4GzB3Izs3sAjNbZ2Ybzey2Q2x3kpl1m9llA9m/xJ8b5k1kR10rC1dWRbXdvc0dXHDnKzyydHtU2xWJJ/HUZ++oa+WuRe+xcXdTvz/z9IoqWju7uaK82MPIRCQRHC6B7llTdEBDN8wsANxF+Mz1NODjZjatj+1+ADw/kP1LfDqrbDiTiqI/vXdhVirHjs7hZZWzE+lVvPXZ+0vYjcrt/02ED1VUUjo8i1lj8zyKSkQSxeES6KOpKXoysNE5tykyDfiDQG9TyX4BeBTYPeDoJe4kJRnXz53I6p0NvPZedGcHnDeliLe37qNxgDcdiSSIuOqzd0YmURmVm96v7TfsauSdbXVcedJYzFT7WUSOziET6KOsKToGqOzxentk3QFmNga4FLj7SIKX+HTJ8WMozErlV1GeWGX+lCK6Qi7qibtInIirPruqLjKJSm5qv7Z/qKKSYJJxyfFjDr+xiMhh9LcO9JHo7U/8g6/Z3wncGplytu8dmd1gZhVmVlFTo0vw8S4tOcCnTyvh5fU1rK2O3uRoJ4wbRmZKgMUb9B0S6UVc9dk1TW0UZqWSGjz8JCqd3SEee3sHZx8znMKs/iXcIiKH4mUCvR3oOc1TMbDzoG3KgQfNbAvhmxR/YWaXHLwj59w9zrly51x5UVGRR+FKNH1ydgkZKQF+vXhz1NpMCSbxh+tO4dYLyqLWpkgcias++9sXH8eir8zv17Yvrd3N3uYOrjxJMw+KyODwMoF+Cyg1swlmlgJcBbxv9kLn3ATn3Hjn3HjgEeBfnXNPeBiTxIi8jPD03k8u33HgZqBoOGHcMLLTBlY3ViRBxFWfbWb9/l1+6K1KhmenMq9UJ2BEZHB4lkBHqnZ8nvCd2muAh5xzq83sRjO70at2JX78c3rv6J2F7uoOcdeijTy/ujpqbYrEg3jrs7/62Er+tmbXYbfb1dDGonW7+diJxQQDXp4zEpFE4mkleefcQmDhQet6vfnEOfdpL2OR2DM2P4MLp4/i/17fxufPnByVM8PBQBIPVVQyuSiL84/VTGQiPcVLn93U3sWf3txGSUEGZx8z4pDbPvb2DkIOrijX8A0RGTz6c1x8dcO8iTS2d/Hgm5WH33iQzCst4rVNe+noiu5siCIyOKoPlLA7dA1o5xwPV1Ry8vh8JhRmRiM0EUkQSqDFVzOK85g9MZ97/xG96b3nTSmipaObiq21UWlPRAbXzrr9k6gcugZ0xdZ9bNrTzOWaeVBEBpkSaPHdZ+dNoqq+jadXHHzDvzdOnVRAMMlYvH5PVNoTkcHV31kIH3qrksyUABdOHxWNsEQkgSiBFt/Nn1JE6fAs7lm8OSrTe2elBjltciHN7QOanV5EYkRLRxeZKQFG5PSdQDe1d/HMyio+PHM0mame3u4jIglIvYr4LinJuH7eRP7jkRUs2biHuVEoNXX/Z07SdL4icerTp0/g6tPGH/J3+JkVO2np6OZy3TwoIh7QGWiJCRfPGs3w7FTuidL03vv/4w2FvD/jLSKD73B/AD9UsZ1JRZmcMC4vOgGJSEJRAi0xITUY4NOnj+eVDXt4d2d0pve+7v63+MrDy6PSlogMnlseXs59/+i7fvzG3Y0s3bqPK08aqytNIuIJJdASMz5xSgmZKQF+/Up0zkJnpyXz9/U1OgstEmeeW1XNlr0tfb7/cMV2AknGpcer+oaIeEMJtMSM3PRkrjxpHE8t38nOulbP25s3pZDa5g5W7az3vC0RGRyNbZ00tnf1WYGjszvEo2/v4Kyy4RRlp0Y5OhFJFEqgJaZcM2c8AHct2uh5W/tvVly8vsbztkRkcFTtL2GX13sN6IcrtrOnqZ1PnDIummGJSIJRAi0xpXhYBp84ZRwPvlXJppomT9sqzErl2NE5qgctEkeqDlEDuq2zm5+/tIETxuUxf4r31XxEJHEpgZaY84WzS0kLJvGjF9Z53ta1cybwkVmjPW9HRAZHKOSYWJTJ6F7OQP/xjW1U1bfxlfOm6uZBEfGU6kBLzCnMSuX6eRO5868beGfbPo4fN8yztj56gm4yEoknZ5YN58yy4R9Y39zexS8WbeS0SQWcNrnQh8hEJJHoDLTEpOvmTqQwK4XvP7vW89kJaxrbeWfbPk/bEBFv3ffqFvY2d/CV86f6HYqIJAAl0BKTslKDfPHsUt7YXMvfPb7J76uPreQLf3onKtOIi8jR+Y9HlvNff1n1vnX1rZ386uX3OLtsOCd4eMVKRGQ/JdASs646aRwlBRn84Nm1dHtYq3n+lEK272tl855mz9oQkcHx9rY6ahrb37fuN69soqGtiy+fN8WnqEQk0SiBlpiVEkziK+dNZW11I39ZtsOzduZNUTk7kXjgnKOqrpWRPSpw7G1q594lm/nQ9FEcOzrXx+hEJJEogZaY9qHpo5g+Jpcfv7Cets5uT9ooKcikpCCDxRtUzk4kljW2d9Hc0f2+EnZ3v/werZ3d/Nu5OvssItGjBFpiWlKScduCMnbUtfLA61s9a2deaRGvvbeXjq6QZ22IyNGpqttfAzpcwm5XQxu/f20rlx5fzOThWX6GJiIJRgm0xLzTJxcyt7SQ/120kYa2Tk/auGHeRF788jxSgvqVEIlVDsfpkwuYUJgJwM9f2kDIOb50TqnPkYlIolG2IHHh1gvKqGsJ32nvhbH5GRQPy/Bk3yIyOMpG5vDH62Zz3JhcKmtbePDNSq48aSxj8/W7KyLRpQRa4sJxY3K5eNZofrtkM7sa2jxpY9G63Xzn6Xc92beIDK47/7qBQJLxhbN09llEok8JtMSNfz93Kt0hx51/3eDJ/tdUNfCbJZtZV93oyf5F5Oh844lVXHH3a2zc3cTj72znX2aXMCIn7fAfFBEZZEqgJW6MK8jgE6eU8FBFJRt3Nw36/q8oH0tBZgo3P/iOZxU/ROTIbd7TTEd3iJ/+dT3pyQFuOmOS3yGJSIJSAi1x5QtnTSY9OcCPnl836PsuzErljstnsLa6kR8+N/j7F5Gjs7O+lczUAM+sqOKaORMoyEr1OyQRSVBKoCWuFGSlcsO8iTy3upq3t+0b9P2fVTaCq08t4d5/bGZZZd2g719Ejoxzjur6NrbtbSEnLch1cyf6HZKIJDAl0BJ3rp0zgcKsVL6/cC3ODf4U31+98Bh+fPlMZhZrVjORWNHQ2kVLRzeV+1r57PxJ5KYn+x2SiCQwJdASdzJTg9x8Tilvbqll0brdg77/tOQAHzuxGDNjd0ObJ0m6iAxMZyjE8OxUctODfPq08X6HIyIJTgm0xKWrThrL+IIMfvDsOrpD3iS4m2qaOPvHL/MHD2dAFJH+Wb+rkd2N7Xzx7Clkpgb9DkdEEpwSaIlLyYEkbjm/jHW7Gnn8nR2etDGhMJPy8cP47jNrVNpOxEfOOX743FpG5qTyiVPG+R2OiIgSaIlfF04fycziXH7ywjpPys6ZGXdcPpPstCBf/JNK24n4ZdG63SyrrKe2uZPkgP7bEhH/qSeSuGVm3LqgjJ31bfzhNW+GWYRL281k3a5Gvv/sWk/aEJG+hUKOHz2/nszUAMMykwkkmd8hiYgogZb4dtqkQuZPKeJ/F22kvrXTkzbOnDqcT582npaOLkIejbcWkd49t7qad6saGJ2Xzui8dL/DEREBlEDLEHDrBWU0tHVy98vvedbGf100jR9eNpMknf0SiZrukOMnL66ndHgWXd2O0blKoEUkNiiBlrg3bXQOl8waw71LNlNV3+pJG/sT5zVVDdz+5GqVthOJgife2cHG3U382zmlVNW3MjI3ze+QREQAJdAyRHz53Ck4B3e+uMHTdt7aUst9r27h/le3eNqOSKLr6Apx59/Wc9yYHM6ZNoIb5k5k/pQiv8MSEQGUQMsQMTY/g0/OLuHhpZVs2OVdybl/mV3CWWXD+d6za1lb3eBZOyKJ7qGKSiprW/n386aSEgzw5fOmMk8JtIjECCXQMmR8/qzJZKQE+d7CNZ4NsTAzfnjZDHLSklXaTsQjbZ3d/PylDZSXDOOMKUU0tnVS29yhoVMiEjOUQMuQkZ+ZwpfOKWXRuhqeXL7Ts3YKs1L50eUzWL+rid/9Y4tn7Ygkqgde38quhnb+/bypmBl/WbaTE779Irsa2v0OTUQEUAItQ8xnTp/AzLF5fOupd9nb5N1/tmdMHc5vry7n2jkTPGtDJFGdMqGAz505iVMnFQBQVd9KIMkoyk71OTIRkTBPE2gzu8DM1pnZRjO7rZf3P2FmKyLLq2Y208t4ZOgLJBl3XDaDxrZObn/qXU/bOvuYEaQEk6hv6fQ0WReJlljps6cX53LL+WUHXlfVtzEiO1WTqIhIzPAsgTazAHAXsACYBnzczKYdtNlmYL5zbgbwbeAer+KRxDFlRDZfOKuUp5bv5IXV1Z621dkd4tJf/IMvP7Rck6xIXIvlPruqro1RmkRFRGKIl2egTwY2Ouc2Oec6gAeBi3tu4Jx71Tm3L/LydaDYw3gkgdx0xiTKRmbzn0+s8myGQoDkQBKfOX08L6+v4T6VtpP4FrN9tmpAi0is8TKBHgNU9ni9PbKuL9cCz3oYjySQ5EASd1w2k73NHXz3GW+HcnxydgnnHDOc7z+7lqVb9x3+AyKxKWb77M+fVcoV5WOj0ZSISL94mUD3Nlit12vcZnYm4c741j7ev8HMKsysoqamZhBDlKFsenEu18+dyEMV23llg3ffGzPjBx+bQVF2Klf86jUWrdvtWVsiHorZPvuyE4s1iYqIxBQvE+jtQM9TBsXAB2qLmdkM4DfAxc65vb3tyDl3j3Ou3DlXXlSkTlT670vnlDKxMJPbHl1Jc3uXZ+0UZKWy8ItzuW7uBGZPCFcOUI1oiTMx2Wc3tHWyake9fp9EJKZ4mUC/BZSa2QQzSwGuAp7suYGZjQMeA/7FObfew1gkQaUlB/jBZTPYWd/KHc+v87St3IxkvrrgGNJTArR2dHPh/7zC7U+u9jRxFxlEMdlnv7mplot+voQ1VZr5U0Rih2cJtHOuC/g88DywBnjIObfazG40sxsjm/0XUAD8wsyWmVmFV/FI4jppfD6fml3C/a9toWJLbVTadDjmlBZy36tbOP/OxSzZsCcq7YocqVjts6vqWwEYrSocIhJDLN6mRi0vL3cVFcqzZWCa27s476eLSQ0msfDmuaQlB6LS7puba7n10RVs3tPMleVjuf0jx5KeEp22JTaZ2VLnXLnfcUTL0fbZP3xuLfcs3sS67yxQHWgRibq++mzNRCgJITM1yPc/Np1Ne5q5868botbuyRPyefbmuXx2/kQ272kmJahfOZGBqKpvY0ROmpJnEYkp+t9cEsbc0iKuKC/m169sYuX2+qi1m5Yc4KsLjuH/rj+FQJKxp6mdrz62QrMXivRDVX0ro1QDWkRijBJoSShf/9A0CjJTuOWR5XR0haLadjAQ/nWr2FLLI0u3c+5PF/Pk8p3E2zAqkWj68rlT+bdzp/gdhojI+yiBloSSm57Mdy45jrXVjdz98nu+xHDBcaN4+gtzGZufwRf/9A7X/34puxrafIlFJNadPCGf0ycX+h2GiMj7KIGWhHPesSO5aMYofv7SBtbvavQlhqkjs3nsptP4+oXHsGRjzYESe6GQ0xlpkYiWji5efHcXNY0a7iQisUUJtCSkb33kWLLTkrnlkRV0h/xJWANJxvXzJvLczfO4bUEZAC+vr+H077/ELQ8v5y/LdihxkIS2qaaZ639fwdKt0Sk/KSLSX0qgJSEVZKXyzQ9PY3llHfcu2exrLOMLMynMSgUgJz3IrHF5vPDuLm5+cBknffevXHDnYnY3hod46Oy0JJKq+vD3flSuakCLSGwJ+h2AiF8+MnM0Ty3fyY9eWMe500YwvjDT75A4sSSfE0vy6Q45Vu+sZ8nGPSzbVkdhZjjB/tZT7/JuVQNzJhdy+uRCZhbnHrg5UWSo2T+JiqpwiEis0f+8krDMjO9cMp2UQBK3PrqCkE9DOXoTSDJmFOfxr2dM5p5PlZMUqYFbPCyd1o5ufvrX9Xzsl69y/H+/yNceX3ngc7H0bxA5Wjvr2kgO2IErNCIisUJnoCWhjcxN4+sfOobbHlvJ/725jU/OLvE7pEO6bu5Erps7kX3NHby2aS9LNu4hPyMFCA/vmPODlxiRm8bM4jxmjc1j5tg8xhdkYKZJKCT+VNe3MiIn7cAfkCIisUIJtCS8K08ay1MrdvL9Z9dyVtlwRufF/njLYZkpXDh9FBdOH3VgXUd3iItmjmZZZR1/fquS+17dAsBNZ0zi1gvKaO/qZsmGPcwcm6czehIXvnzuVPY060ZaEYk9SqAl4ZkZ3//oDM776WK++thKfnt1eVyOK04NBvjahccA0NUdYsPuJpZX1nHMqBwA1lQ1cu39FUB4KMjMsXkcPzaPBdNHMSYO/miQxDOuIINxBRl+hyEi8gHxlyWIeGBsfga3LSjj5fU1XPKLf0R1qm8vBANJHDMqh6tOHsfMsXkAlI3M5qHPnsrXLixjZnEey7bV8Z1n1rB1bzMAS7fW8t9PvcvClVWa2EV8Fwo5/vDaFt9qtYuIHIrOQItEfOrUEgqzUrn9qdVcfNcSPnP6BL587hQyU4fGr0lacoCTJ+Rz8oT8A+tqGtvJSQ//+9bvauKPb2zl3n+Ey/oVD0unvGQY37r4OHLTk32JWRJXbUsH3/jLam7/8DSmjMj2OxwRkfcZGpmByCAwMz40YxRzSgv5wXNr+e2SzTy3qppvX3IsZ5WN8Ds8TxRl/3Ms9MdPHsfHTijm3aoGKrbU8va2fazcUU9W5A+I/2/hGt6tauCEccMoHz+MWWPzyE5TYi3eqKqL1IDW8CIRiUFKoEUOkpuezPcunc6lx4/ha4+t5Jr7KvjQ9FF888PTGJ4ztOvRpgSTmDU2XMHjYHkZKexp6uDnL20g5MAMzpw6nHs/fRIAK7fXU5CVwqjcNFX9kKOmGtAiEsuUQIv04aTx+Tzzxbn86uX3+PmijSzeUMNtC8r4+EnjErKs1k1nTOKmMybR2NbJsso6KrbsIzX5n7dRXP/7Cqob2shMCTBpeBaTi7I4o2w4H5k5GoDukCOQgMdNjoxmIRSRWKYEWuQQUoJJfOHsUj40YxRff3wVX398FY+/vYPvfXR6wo7LzE5LZm5pEXNLi963/s6rZrFxd9OB5dX39pKTnsxHZo6mszvEjNtfoHhYOpOKspg8PLycWDKMsfkZhEIOB0qw5YCq+jZSAkkUZKb4HYqIyAeYc/E1c1l5ebmrqKjwOwxJQM45Hlm6ne8uXENzexc3zp/E586cTFpywO/QYlYo5EhKMprau7hr0UY27m7ivd1NbK1toTvkuPWCMm46YxJb9jRzxo/+TnpygKy0INmpQbLSgnzuzMmcf+xIdtS1cs/L75EZWZ+dGiQzNcjsiQWMzktnX3MHK3fUE0wygoEkAklGcsAYX5hJTloyze1d7G3qIBCw8DZJRiDJyEoNEgwk0dUdoityhjzJjCTDs2EoZrbUOVfuyc5j0JH22U3tXexqaGNSUZYHUYmI9E9ffbbOQIv0k5lxeflYziobznefWcPPX9rI0yuq+O6lx3HapEK/w4tJ+4e6ZKUGufWCsgPr27u62bq35UB1j6y0IP92zhSa2jtpau+isa2LpvYuUoPhISJ7Gtv5y/KdNLV10dVjuvJffOIERuels3JHPZ+6980PtH/fZ07ijKnDWby+hpv++PYH3n/0plM5sSSfx9/ZwS2PrHh/7AYLb55L2cgc/vjGVr77zBoCZpjBaZMKuftfTjz6AyR9ykoNkqXkWURilM5AixyhVzbU8PXHV7GttoXLTyzmaxcewzBdbvaUc472rtCBJLswK4XstGTqWzvZuLuRzm5Hd8jR2R2iO+QOzLq4fV8Lb2yqDb8XCr/X1e24aMYohueksaaqgZfW7iYUcoQcdDuHc45PnTqeouxU3tpSy/Orqgk5CDnHhMJMrj5t/BH9G3QGun9+8feNTB+T+4GhQiIi0dRXn60EWuQotHZ087OXNvDrxZvISU/mo8ePYda4cBWLMXnpqkYhH6AE+vBCIcfUbzzLtXMmctuCssN/QETEIxrCIeKB9JQAt15QxsWzRvPdZ9bw+9e38psl4YlICrNSmTU2j+MjCfWM4lzVTRbphz3N7XR2O0bnqYSdiMQmJdAig6BsZA5/uPYUOrpCrK1uYFllHcu21bGsso6/rtkFhOsmTy7KCtdZjiTVU0dkEwwkHWbvIomlOlLCbuQQr7suIvFLCbTIIEoJJjGjOI8ZxXl86tTwuvqWTpZtr2N5ZTih/tva3Ty8dDsA6ckBpo/JZda4cDKdn5XCsIwU8jNSGJaZTFZqUMNAJOHsrwE9WrMQikiMUgIt4rHcjGTmTyli/pTwzVDOOSprW3mncl/4THVlHff9Ywsd3aEPfDY5YOGEOjPln4+ZyQzL6Pk6hYLMFIqHpZOXoZsYJf7tboicgdYshCISo5RAi0SZmTGuIINxBRlcPGsMEC7rtrOujX0tHexr7qC2uYN9LR3UNndS1/LP12urG9jX0sm+lg56u/83Jy1ISUEm4woyKMnPYFx+uJ2SgkxG5qRpohKJC/9y6nguPn4M2an6L0pEYpN6J5EYkBoMMKEwkwlk9mv77pCjobWT2pYO6lo6qGnsoLK2ha21zWyrbWX1jnqeX1X9vprJKYEkivPTGZcfSa4LMinJz6CkIIPhOWlkpwYTcopyiU05uuFWRGKYEmiROBRIMoZFhm/0pas7RFV9G1v3RhLrvS1sq21h694WKrbso6m9633bJxnkZaSQl55MXkZ4mEhuxv7hIsnkRh7z0lPC72eGt81ICWictoiIJBQl0CJDVDCQxNj8DMbmZzCH98+U6JyjtrmDrbUtbNvbwp6mdva1dFDX0hleWjuoqm9jbXUj+1o6aOno7rOdlEDSgYR7/+OwzGTy9ifcPW6KzIuM3c5NT9ZwEhERiVtKoEUSkJlRkJVKQVYqJ4wbdtjt27u6qW/pZF9LeEz2vpZO6ls7DozHrmvuPJCAv1fTxL6t4e16DiF5f/vhS/TDMpLJTU8mJ7LkRpactB7P04MHnuemJ5Od5n/y3R1yhJwjWSUIRUQSkhJoETms1GCA4TkBhg+gLq9zjqb2LuoiSXZtc8eB5z0T8YbWTupbO9mxr5WGtvDzzu5Dz5CanRokJzJ8JDmQREowiZRAEslBIzmQFF4XSCI5EHkdPOh1IIlAktHe1U1bZ4i2zshjVzftnT3WHfR+e2RdZ7fjlvOn8rkzJx/toRURkTikBFpEPGFmZKeFzxiPzc/o9+ecc7R2dtPQ2kV9a2c4qW7p/Ofz1n8ubZ3ddHQ5OrtDdHaHaO8M0dTWRUf3P9d1doXo6A7R0RWiM7J+/5nxJIO05EB4CSaRlhIgLRggLTmJtOQAOenJ4efBAKnJ/1yfFgxw8oR8rw6diIjEOCXQIhJTzIyMlCAZKUHP6gCHQo5u5wgmmW6AFBGRAVMCLSIJJynJSEKJs4iIHBndASMiIiIiMgBKoEVEREREBkAJtIiIiIjIACiBFhEREREZACXQIiIiIiIDoARaRERERGQAPE2gzewCM1tnZhvN7LZe3jcz+1nk/RVmdoKX8YiISN/UZ4uI9I9nCbSZBYC7gAXANODjZjbtoM0WAKWR5Qbgl17FIyIifVOfLSLSf16egT4Z2Oic2+Sc6wAeBC4+aJuLgd+7sNeBPDMb5WFMIiLSO/XZIiL95GUCPQao7PF6e2TdQLcRERHvqc8WEeknL6fy7m2eXHcE22BmNxC+XAjQZGbrjiCeQmDPEXzOa7EaF8RubLEaF8RubLEaF8RubF7FVeLBPgeD+uz+i9XYYjUuiN3YFNfAxWpsUe2zvUygtwNje7wuBnYewTY45+4B7jmaYMyswjlXfjT78EKsxgWxG1usxgWxG1usxgWxG1usxuUh9dn9FKuxxWpcELuxKa6Bi9XYoh2Xl0M43gJKzWyCmaUAVwFPHrTNk8CnInd2zwbqnXNVHsYkIiK9U58tItJPnp2Bds51mdnngeeBAHCvc261md0Yef9uYCFwIbARaAE+41U8IiLSN/XZIiL95+UQDpxzCwl3uD3X3d3juQM+52UMPRzV5UQPxWpcELuxxWpcELuxxWpcELuxxWpcnlGf3W+xGlusxgWxG5viGrhYjS2qcVm4PxQRERERkf7QVN4iIiIiIgMw5BLoWJyK1szGmtkiM1tjZqvN7OZetjnDzOrNbFlk+S+v4+rR9hYzWxlpt6KX9/04ZlN7HItlZtZgZl86aJuoHTMzu9fMdpvZqh7r8s3sRTPbEHkc1sdnD/md9CCuO8xsbeRn9biZ5fXx2UP+3D2K7XYz29HjZ3ZhH5+N9jH7c4+YtpjZsj4+6+kxS0Tqs48oPvXZh49HffbgxKY+uy/OuSGzEL7x5T1gIpACLAemHbTNhcCzhOuZzgbeiEJco4ATIs+zgfW9xHUG8LRPx20LUHiI96N+zHr5uVYDJX4dM2AecAKwqse6HwK3RZ7fBvygj9gP+Z30IK7zgGDk+Q96i6s/P3ePYrsd+Eo/ft5RPWYHvf9j4L/8OGaJtqjPPuL41GcfPgb12YMTm/rsPpahdgY6Jqeidc5VOefejjxvBNYQX7N3+T1979nAe865rVFs832cc4uB2oNWXwzcH3l+P3BJLx/tz3dyUONyzr3gnOuKvHydcK3eqOvjmPVH1I/ZfmZmwBXAnwarPTkk9dneUJ+tPnvA1GcPzFBLoGN+KlozGw8cD7zRy9unmtlyM3vWzI6NVkyEZxJ7wcyWWngGsYP5PX3vVfT9y+HXMQMY4SI1cCOPw3vZxu9jdw3hM1G9OdzP3Sufj1yqvLePS6h+HrO5wC7n3IY+3vfrmA1V6rOPjPrsI6M++8ioz+7FUEugB20qWi+YWRbwKPAl51zDQW+/Tfhy10zg58AT0Ygp4nTn3AnAAuBzZjbvoPf9PGYpwEeAh3t5289j1l9+HruvA13AH/vY5HA/dy/8EpgEzAKqCF96O5hvxwz4OIc+k+HHMRvK1GcfGfXZ3lGf/X7qs/sw1BLoQZuKdrCZWTLhjviPzrnHDn7fOdfgnGuKPF8IJJtZoddxRdrbGXncDTxO+HJMT74cs4gFwNvOuV0Hv+HnMYvYtf+yaORxdy/b+PV9uxq4CPiEiwwEO1g/fu6Dzjm3yznX7ZwLAb/uo02/jlkQ+Cjw57628eOYDXHqs4+A+uwjpj57gNRn922oJdAxORVtZIzOb4E1zrmf9LHNyMh2mNnJhH82e72MK9JWppll739O+GaGVQdt5uf0vX3+denXMevhSeDqyPOrgb/0sk1/vpODyswuAG4FPuKca+ljm/783L2Irec4zEv7aDPqxyziHGCtc257b2/6dcyGOPXZA49NffaRU5898NjUZ/elr7sL43UhfPfxesJ3hH49su5G4MbIcwPuiry/EiiPQkxzCF/OWAEsiywXHhTX54HVhO9efR04LUrHa2KkzeWR9mPimEXazSDcueb2WOfLMSP8H0IV0En4r+1rgQLgb8CGyGN+ZNvRwMJDfSc9jmsj4fFo+79rdx8cV18/9yjE9ofId2gF4Q52VCwcs8j6+/Z/t3psG9VjlohLbz9rv/sf1GcfaWzqs48sLvXZA4wrsv4+fOyzNROhiIiIiMgADLUhHCIiIiIinlICLSIiIiIyAEqgRUREREQGQAm0iIiIiMgAKIEWERERERkAJdAyJJlZt5kt67HcNoj7Hm9mqv8rIjJI1GdLvAn6HYCIR1qdc7P8DkJERPpFfbbEFZ2BloRiZlvM7Adm9mZkmRxZX2JmfzOzFZHHcZH1I8zscTNbHllOi+wqYGa/NrPVZvaCmaX79o8SERmi1GdLrFICLUNV+kGXA6/s8V6Dc+5k4H+BOyPr/hf4vXNuBvBH4GeR9T8DXnbOzQROIDybEUApcJdz7ligDviYp/8aEZGhTX22xBXNRChDkpk1Oeeyelm/BTjLObfJzJKBaudcgZntITxFaWdkfZVzrtDMaoBi51x7j32MB150zpVGXt8KJDvnvhOFf5qIyJCjPlvijc5ASyJyfTzva5vetPd43o3uJxAR8Yr6bIk5SqAlEV3Z4/G1yPNXgasizz8BLIk8/xtwE4CZBcwsJ1pBiogIoD5bYpD+ApOhKt3MlvV4/Zxzbn9ZpFQze4PwH5Afj6z7InCvmd0C1ACfiay/GbjHzK4lfNbiJqDK6+BFRBKM+myJKxoDLQklMp6u3Dm3x+9YRETk0NRnS6zSEA4RERERkQHQGWgRERERkQHQGWgRERERkQFQAi0iIiIiMgBKoEVEREREBkAJtIiIiIjIACiBFhEREREZACXQIiIiIiID8P8DvH5ELLOYrgkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(weighted_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "REy6WClTZIwQ"
   },
   "source": [
    "### Evaluate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "nifqscPGw-5w"
   },
   "outputs": [],
   "source": [
    "train_predictions_weighted = weighted_model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_weighted = weighted_model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "owKL2vdMBJr6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.01350308582186699\n",
      "tp :  73.0\n",
      "fp :  71.0\n",
      "tn :  56799.0\n",
      "fn :  19.0\n",
      "accuracy :  0.998420000076294\n",
      "precision :  0.5069444179534912\n",
      "recall :  0.79347825050354\n",
      "auc :  0.9247567057609558\n",
      "prc :  0.7375406622886658\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  56799\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  71\n",
      "Fraudulent Transactions Missed (False Negatives):  19\n",
      "Fraudulent Transactions Detected (True Positives):  73\n",
      "Total Fraudulent Transactions:  92\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "weighted_results = weighted_model.evaluate(test_features, test_labels,\n",
    "                                           batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(weighted_model.metrics_names, weighted_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_weighted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PTh1rtDn8r4-"
   },
   "source": [
    "Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade-offs between these different types of errors for your application."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hXDAwyr0HYdX"
   },
   "source": [
    "### Plot the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "3hzScIVZS1Xm"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f33849a2c50>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TCT85fRQnDO6a0GtH4kdbJCO1g0ft4FE71FFbeJptO7w7F1ZC61Hn0vqEKU2urtm2Qzxcbk2uwq9Rn10Cr32Ac4Anzax7vSLfxLtFurePgAFm1s/MWgDnAy/HO14RERERP/jVN/l84Bm1SuAK59zXZvZPMyvAu/W5CrgUwMx6AA855051zlWZ2Y+AN4BM4BHnXHQLYoqIiIg0M37d+jw6xL7vhim7Hm/AQe3714DX4hediIiISHLQkEIRERGRJKVhGT7ZXL6HyuqaBvt2V1X7FI2IiMRM+SaorvA7iujt3u53BBKBEjUfPPLeSm57JSXm6BURkfo+uB/e+LnfUUgKUaLmg0XrSwFom5tFqxYN/wi6ts1hZJ/2PkQlIiJNtuGTuu02PfyLo7FatIaB3/A7CglBiZqPbjljCOeN7uV3GCIiEmtnPwAF3/Y7CkkBGkwgIiIikqSUqImIiIgkKSVqIiIiIklKiZqIiIhIklKiJiIiIpKkNOozwb7csoMXPl7ndxgiIgKw/HX43y9jMkHtmJ07obosBkGJ1FGilmAzVmwObvfPb+1jJCIiwidPQ0lsJiBvVbthGdB5QEzqFFGilmjOAfCNod0Y1aeDz8GIiKQ773cyJ98BA05qUk2zP5zNEWOOgNz2kJff9NBEUKLmm055LfwOQUREarXp1uResF2t1qknTWJOgwlEREREkpQSNREREZEkpURNREREJEkpURMRERFJUhpMkEAlZbv5zX+X+R2GSHpY9R4sfjE40jrWBqxfD+Uvx6Xu5qZZt8X6+X5HIBKRErUEenbOWnZWVAPQvqVGfYrE1X9vhI0L41Z9T4D1cau+WUmJtmip6ZIkOSlRS6A9lV6S1qtDSyYf29/naERSXNVu7/WY6yGvS8yr/3TFCgYO0FQMkAJtkdcF+h3rdxQiISlR88HE0b1pm5vtdxgi6WHYRMgfGPNq1+8sZuCYopjX2xypLUTiR4MJRERERJKUEjURERGRJKVETURERCRJKVETERERSVIaTJBAC9eV+h2CSPzU1MCqd2H3Nr8j8VSU+x2BiEiTKVFLkNVbdvL28k0AZGWaz9GIxMHSl+HZi/yOYl+Z+jUnIs2XfoMlyKbyPcHts0f29DESkTjZ4f1HhPYHQffh/sZSK/9Q6NDP7yhERA6YErUEG9mnPT3bt/Q7DJH4GXAinPZ7v6MQEUkJGkwgIiIikqSUqImIiIgkKSVqIiIiIklKiZqIiIhIktJgggTZU1ntdwgiTVe5i6zKctj1dYhjOxMfj6SNz0rKeejdL8Ie//FJg8hvkwPAv2av5pO120KW65/fmsnHHAzAropqfvmfxWHrPH9MHwp6twfgnU838d+FG0KWy83OZMqZQ4Lvf/fGMraUV4Qse/SAfE4b3j2lP9Mji/bw+pZPUuozxeLP6UApUUuA+Wu2ccFDs/0OQ6RpVr0H/zyHo6r3wPt+ByPppmT7bp76aE3Y45cee3DwH8uZn2/mlU9C/2M9tn+nYAJQWVMTsc5xh3QOJgDLNmwPW7ZNTlaDBODVTzawakvo/7i0bZkdTABS9TPNWFsFa0PX21w/Uyz+nA6UErUEWLy+bkWC04Z19zESkSZYPx+qA/MB5rYLXSarJQw4OWEhSWqrqq7hk3WlZJrRPz+P35wzLGzZTnktgtvfHtOH8Yd0Dlmua9uc4HZuVmbEOkf0qvueHz0gn7Yts0OWy8poOIn5j08aRPmeqpBlD+3WJridqp/p4iEtGDRoUMiyzfUzHeif0wW/DXta1JSoJdC3x/ThB0f39zsMkSZZ0+tMev/gn36HIWmgfE8V5/xlJu1aZrPg1pP49pg+UZ03/pDOjI+iXIusjKjrHNyjLYN7tI2q7BkjekRVrlu73JT8TEW9symKIobm9JkO9M/pgqjOikyDCURERESSlBI1ERERkSSlRE1EREQkSSlRExEREUlSStREREREkpRGfcbYS/PX8dC7K6lxLrhv647QE+qJ+G7OIzD3Uaj3fQ1rx6a4hyMiIg0pUYuxx2d9ycJ1pSGP9e3UKsHRiOzHrL/C5k8bdcqulpoLUBIjLyeL1646msy95r8SSSdK1GKstmPit+cOY0iPukn4crIyOKRLnk9RiYRR+4U95yHoPGD/5bNbsX7ROgbGNyoRALIyM6KeE0skVSlRi5P++XkM7Rlm9naRZNN9BORHmX7Z+vjGIiIiQRpMICIiSWnHnip++uwCbn1pkd+hiPhGiZqIiCSlyuoanp27ln/PVy+upC8laiIiIiJJSomaiIiISJLSYIIYqayu4dH3V7Hm651+hyLpYvGLsG5u0+rQ3GgiIklNiVqMfPD5Fm5/bWnwfZtcNa3E0Z5yeO774KpjU19Om9jUIyIiMaVsIkZ2VXr/YA7oksc1JwxkUFf9wydxVF1Rl6SdeFvT6uo8CNpqElsRkWSkRC3G+nZuzWnD9Y+eJEhuexh/td9RiMRFRoZR0Lu97lBIWtO3X0REklLb3Gz+fcV4v8MQ8ZVGfYqIiIgkKV8SNTO72swWmdliM7smsO93ZrbMzD4xsxfNrH2Yc1eZ2UIzm29mcxIZt4iIJI5zjqrqGqqqa/wORcQ3CU/UzGwo8P+AMcAI4HQzGwC8CQx1zg0HPgV+FqGaCc65AudcYdwDFhERX5TuquSQm/7L6F9P9zsUEd/48YzaYcAs59xOADN7B/imc+6uemVmAef5ENsBKd1ZyeL12/0OQ+KlZCmUrqPjlk9gRZXf0XgqyvyOQEREEsCPRG0RcLuZdQJ2AacCe9/CvAR4Osz5DphmZg540Dk3NW6RRun7j33EnC+/BiArw3yORmJq82fwlyMBGA6w0Ndo9pWh8UAiIqnMnHOJv6jZ94ErgHJgCbDLOXdt4NhNQCFwjgsRnJn1cM6tN7MueLdLr3TOzQhRbjIwGaBr166jn3rqqUbFWF5eTl5eXlRlf/LOTjbvcgzulMFZB7dgUMfMRl0r2TWmLVJN+68XUrDgF1Rm5fF1q/5kZSbXn+2m/HFs6HFSQq+Zzt+H+tQOdeLVFuUVjh+9tZPW2XD/8a1jXn+s6TvhUTvUmTBhwtymPqbly3/HnXMPAw8DmNkdwNrA9kXA6cDxoZK0wLnrA68lZvYi3rNu+yRqgZ62qQCFhYWuqKioUTEWFxcT7Tm5s9+CXbt48PvH0rtjq0ZdpzloTFuknJWZsACyexawpN9Pk64dOgKDEnzNtP4+1KN2qBOvtti2swLeepOsrOxm0db6TnjUDrHlS6JmZl0CiVYf4BxgrJmdAtwAHFv7/FqI81oDGc65ssD2SUATp2UXEZF/frCK5z9ex2Hd2/Cbc4YDsLuymvOnzgp7ztXHD2DCoV0A+M+C9Tz83sqQ5VpkZfDMpWOD7698ch5rtoZeF/n04d35wdH9AViyQc/+ivj1gMvzgWfUKoErnHNfm9mfgRzgTTMDb8DBZWbWA3jIOXcq0BV4MXA8C/iXc+51fz6CiEjqeOCdL1i3bdc+z9nOX7Mt7Dlbd1QEtzeV7QlbNier4QQDyzZsZ0VJeciyBb3bB7cNL5beHVtGiFwktfl16/PoEPsOCVN2Pd6AA5xzX+BN6SEiIjFUE3ja5LoTBwb3tcjM4IXLx4U956B6j3qcPrw7BX3ahyy39xCrP50/kt1V1SHL5uflBLeH9mzLC5eP49BuWjtZ0peGjImISFDfznUP7WdkGKP6dIjqvC5tc+nSNjeqsoN7tI2qXJvc7KivL5KqlKjFgA8DZ6W+6ipwcZq5vKYyPvWKiIhEQYlaE23fXcm6bbv8DiN9LX0FnrsEqvf4HYmIiEjMKVFropWbdgS3u7WLrttfYmj1B16SZhlgcZrjLCMTBp7sDX0RSVEtsjJokeXL8s8iEoEStRgZ3qsd2Zn6JeebE34J46+K7zWKi+Nbv4iP3vnpBL9DEJEQlFmIiIiIJCklaiIiIiJJSomaiIhwwd9mceIf3qFk+26/QxGRevSMmoiIsHLzDjaU7qaqRvMNiSQT9aiJiIiIJCn1qDXRfW+t8DuE1FZTA69cDZvDtPPXqxIajoiISCIpUWuCmhrH9KUlAPRsr0WD42LLCvj4H/sv1753/GMRERFJsLCJmpmNiuL8SufcwhjG02z9cVKB3yGkptqlodr1gXMeDF0mtx10GZy4mERERBIkUo/aO8BHgEUo0w/oG8uAmiMzyM2O06z44mnRCg4a53cUIiIiCRUpUfvIOXdcpJPN7K0YxyMiIj6YWNib7bsqad1CT8SIJJOwfyP3l6RFW0ZERJLfdScO9DsEEQkh6v86mVk+cDXQEvirc+6zuEUlIiIiIo2aR+33wAzgdeDJ+IQjIiJ+WLBmG3NWbWVPVbXfoYhIPZFGfb4O3O6cezewqwWwCnBATvxDS24VVTU8+eFqv8NITVtXwsLnoKYKdmzyOxqRtHDZ43PZULqbmTceRw9NNySSNCLd+pwE3GxmPwRuDvzcinfr8/IExJbU3l2xiVtfXgxAnh6+ja3/3QaLX2i4r0WeP7GIiIj4KNJgglLgJ2bWH7gdWAdcEdif9sr3VAW3H/zuaB8jSUEV5d7r0POg0yHe/CeHnuZvTCIiIj6IdOuzP/BDoBL4MXAw8IyZvQL8xTmnBxmAM0b0YNwhnf0OIzUN/xYMPNnvKERERHwTaTDBk3gDB2YB/3TOveucOxnYDkxLRHAiIiIi6SzSw1W5wEqgNdCqdqdz7jEzeybegYmIiIiku0iJ2g+B3wEVwGX1DzjndsUzKBERERGJPJhgJjAzgbGIiIhPHv/BEVTXOPLbpP3sSyJJJdJggqnOucmRTo6mTKrauH233yGkrkp12ErsfbJ2G5MenBX2+AuXj+Ow7m0B+MW/F/L83HUhyw3r2Y5nLhsLQHWNY+itb4St87azhjCxsDcAT364mtv+syRkuQyDxbedEnx/9v3vs/yrspBlJx3emylnDonbZxKR5BLp1ufZZhYpGzFgQozjaRZKd1Vyx2vLAMg0n4NJRave815rqiKXE2mEGge7KsMPVq9xLrhdWeXClt175v5IdVbX1NVZVRO+zoy9fo/sqaoJW7ayuqZezPH5TCKSPCIlaj+N4vx3918k9Wwp3xPc/vaYPj5GkqK6HAYlS6B1vt+RSAp4+qPV7NhTzXmFvVhyW/jpXnKyMoPbt509hFvPHByyXIZZvW0i1pmdWTew/vzDe3PuqJ5Rxfzi5eMaJFn1ZdbL6ob1bBfzzyQiySXSM2qPJTKQ5qhf59Yc0b+T32Gkrhat/Y5AUsAf31zBV9t3841h3ejeLrqlkeonOJGYGa2iXJkkOzOjQeIWSW52dNfPzIj++tF+JhFJLo1ZlF1EREREEkiJmoikNEfoW4giIs1BoxI1M8sws7bxCkZEJF4MPYclIs3PfhM1M/uXmbU1s9bAEmC5mUUz0EBExHdhnskXEWkWoulRG+yc2w6cDbwG9AG+G8+gRERiTQMbRaQ5ima4ULaZZeMlan92zlWaWVr+H3V3ZTVn3/8+K0rK/Q7Ff7MegOm3xmeuM82fJjHUIiuDFll6HFdEmqdoErUHgVXAAmCGmR0EbI9nUMlq7dc7WVZvtvCjDunsYzQ+WzENquK4OkOHvt6PSBO9d8NxfocgInLA9puoOefuBe6tt+tLM0vLFQlq9c9vzRvXHBP1nEgp7dtPwyHHx77ejCzdqxIRkbS330TNzLoCdwA9nHPfMLPBwFjg4XgHl6wMlKTVysiCzGy/oxAREUlJ0WQbjwJvAD0C7z8FrolTPCIiMfWtBz/gxD+802DpNxGR5iKaRK2zc+4ZoAbAOVcFaAVfEWkWvti0gxUl5VRrng4RaYaiSdR2mFkn8Kb3NrMjgdK4RiUiEjNK0ESk+Ypm1OePgZeBg83sfSAfOC+uUYmIxJhWJhCR5iiaUZ9zzexYYBDec/TLnXOVcY8syVRV13DTi4v8DiPx1nzE0IW/hjX3Nty/fp4/8YiIiKSRaEZ9LgCeBp52zn0e/5CS06L125m9cisA3drl+hxNAn30Nzpv+Qi2hDnetntCwxFpLD2aJiLNWTS3Ps8EJgHPmFkNXtL2jHNudVwjSzLVNTXB7Qe+M9rHSBKsdpWA8ddA36MbHmvbHboOSXhIIgdC0/KJSHMUza3PL4G7gLvMbABwM/BbIDPOsSWlUX3a0yY3DecN6zYMBpzgdxQijXb+mN7s2FNNbnZa/soSkWYumh41zKwv8C28nrVq4Po4xpSUdPtEpHn66cmH+h2CiMgBi+YZtdlANvAsMNE590Xco0pipvsnIiIikiDR9Khd5JxbFvdIRETiYN7qr6mucYzo3V5Lv4lIsxM2UTOz7zjnHgdONbNT9z7unPtDXCNLMrrzKdI8XfLoR3y9s5KPbz6Rjq1b+B2OiEijROpRax14bRPiWNrmLSl747NyN3zyNOze1nD/pk99CUckVtL2l5WIpISwiZpz7sHA5nTn3Pv1j5nZ+LhGJYm35N/wn6vCH89ulbBQROIhZf+TJSIpLZpn1O4DRkWxL6Wl/KjP3du9127DoH9Rg0Off7Wdgw85PvExicRAyv/dFZGUFukZtbHAOCDfzK6rd6gtaTqHGqTBpJm9j4STft1g15riYg7OyvEpIJF9rdu2i6rqmpDH2uRmB59F211ZTekub8W7lP+7KyIpKVKPWgsgL1Cm/nNq29Gi7CLio4se+ZDPSspDHrt4XF+mnOmtmLFwXWkiwxIRiblIz6i9A7xjZo8GVidIa073T0R8M+b26ZSU7WH2z4+na9tcurfLpaIqdI9ah1Z1IztzsjLo07EVow/qQLuWabiiiIg0e5Fufd7jnLsG+LOZ7ZOlOOfOjGdgycr0SLKI7/75/SOiKje8V3tmXD8hztGIiMRPpFuf/wy83h3ri5rZ1cD/wxuI9Tfn3D1m1hFvwfe+wCrgW865r0OcewrwJ7zn5B5yzt0Z6/hEREREkkGkW59zA6/v1O4zsw5Ab+fcJwd6QTMbipekjQEqgNfN7NXAvv855+40sxuBG4Eb9jo3E7gfOBFYC3xkZi8755YcaDzRatY3Pp2DkqVQXRG+zPa1iYtHREREohLNWp/FwJmBsvOBTWb2jnPuukjnRXAYMMs5tzNQ/zvAN4GzgKJAmceAYvZK1PCSu89q1xs1s6cC58U9UQtqjnc+37kLiu+IrqxpiR0REZFkEc08au2cc9vN7AfA351zt5rZAfeoAYuA282sE7ALOBWYA3R1zm0AcM5tMLMuIc7tCayp934tEN3DKuls6xfea7ve0LJD+HLZLWHYxMTEJCIiIvsVTaKWZWbdgW8BNzX1gs65pWb2W+BNoBxYAFRFeXqo/qyQdyXNbDIwGaBr164UFxc3Ks7y8vIG5yzdUg1A6bZtja7Lb4du/IpuwNLu57Cx23GRC3++Az4vbrBr77ZIV2oHjx/tUFHh3bafOXMmHXKTo9dX34c6aguP2sGjdoitaBK124A3gPedcx+ZWX9gRVMu6px7GHgYwMzuwOsZ22hm3QO9ad2BkhCnrgV613vfC1gf5hpTgakAhYWFrqioqFExFhcXU/+cnM+3wEezaN++PUVFYxtVl++2Pgkb4bBDD+OwgqJGn753W6QrtYPHj3a4pe1adlVWc/LInrRqEc2vrfjT96GO2sKjdvCoHWJrv7/xnHPPAs/We/8FcG5TLmpmXZxzJWbWBzgHGAv0Ay4C7gy8vhTi1I+AAWbWD1gHnA9c0JRYRCT5nTOql98hiIj4Yr/3EMysl5m9aGYlZrbRzJ43s6b+1nzezJYA/wGuCEzDcSdwopmtwBvVeWfg+j3M7DUA51wV8CO8Hr6lwDPOucVNjCUqrnmP+xQREZFmKJp7CH8H/gXUPmX+ncC+Ew/0os65o0Ps2wLss/K3c2493oCD2vevAa8d6LWbSusFiiTei/PWsquihrNH9kiaW58iIokQzW+8fOfc3+u9f9TMrolTPCIi+/jNa8soKdvD8Yd1UaImImklmt94m83sO8CTgfffBrbEL6Tk45zjqifnJfaiS16CV38MVREmqY1W5Y6m1yEiIiIJF02idgnwZ+CPgffvB/aljYrqGjaXewlT4UEdE3PRT9+AHZtiV192a+g2NHb1iYiISNxFM+pzNd7KBAL85ORBib3gyXdAwYVNrycrF7Jzm16PiIiIJEw0S0j1x1sE/Ui8yWU/AK6tXcYpnbTI9GGizdx20LJ94q8rIiIivosm8/gX8AzQHeiBN6fakxHPEBEREZEmiyZRM+fcP51zVYGfxwmzbJOIiIiIxE40gwneNrMbgafwErRJwKtm1hHAObc1jvGJiDD75/tMsSgikhaiSdQmBV4v3Wv/JXiJW/+YRiQishfTTNMikqaiGfXZLxGBJLOKqprEXnDWAzD/icReU0RERJKOD8MYm59P1pYC3nxqcVe1B16/se59Xtf4X1Mkyf3fIx9yxn3vsaV8j9+hiIgklNZiiUJVjTd24uD81vG/mKshOFbj4lehz7j4X1MkyS3bsJ2Ssj3Bv4siIulCiVoUnPP+cejRvmXiLpqVC32PStz1REREJOns99aneb5jZrcE3vcxszHxDy356IFmERERSaRonlH7CzAWbzF2gDLg/rhFlIR0s0VERET8EM2tzyOcc6PMbB6Ac+5rM2sR57iSkvrTREREJJGiSdQqzSyTQMeSmeUDCZ6vwmfqUpMEcs4xf802Ssr2MKRHW3p1aAXAmq07WbJhe9jzTh7SLbg98/PNlO2uClmuV4eWDOnRDoDSnZXMWrklbJ1jD+5E29xsABatK2Xdtl0s2ljFnsVfNSjXNjebsQd3CsY/bcnGsHUe6GcSEUlH0SRq9wIvAl3M7HbgPOAXcY0qScXtEbX182DtHG+7ujJOF5Hm4pO1pXzzLzMB+O25w5h0eB8Aij/dxM3/XhTynAyDL35zWvD9r15ZytIwCdCFR/Th9m8OA2Dllh1c+s+5YWN59aqjgkndPz5YxTNz1noH5jU8Z0Svdrz0I2/wS40jYp0H8pnOHtmTst2V5GZnhq1XRCQVRTPh7RNmNhc4Hu/u39nOuaVxjyyJuHh2qdXUwGNnwp69/lHNyo3fNSWpbSrz5grrnJfTYKRxrw4tOXFw6Hn1Mvb6T8TY/p3o1SH0KOXDurcNbrfNzQpbp3c8O7g9pEc7ThxcyebNm+ncuXODcv06N5y6JlKdB/KZfn7qYWHrExFJZftN1MysD7AT+E/9fc651fEMLBnFpUPN1dQlaYXfr9t/iNY2THcjerXj6AH5wfcTBnVhwqAuUZ17yxmDoyrXPz+Pv/1fYVRlLxrXl4vG9aW4uJiiovDnZGZY1HU25jOJiKSjaG59vor3lJYBuUA/YDkwJI5xJRWXiGfULBNO/0MCLiQiIiLNRTS3PofVf29mo9h3gfa0oHnUREREJJEavdanc+5j4PA4xJK0EtKjJiIiIrKXaJ5Ru67e2wxgFLApbhElMfWnSSIc2r0Nd3xzGD3aa0CJiEi6i+YZtTb1tqvwnll7Pj7hJCd1qEki9erQiguO6ON3GCIikgQiJmqBiW7znHM/TVA8Sam6xkvVmvyIWsVO2L6u4b6a0JOSioiIiIRN1MwsyzlXFRg8kNZuecmbkLOmKV1rNdXwlyNgW5hZTTRQQQLWfr2TGZ9upkf7XIo0dYWISFqL1KP2Id7zaPPN7GXgWWBH7UHn3Atxji1ptGrhzYZ+eN+OB15J5a66JK3TIfseH3jKgdctKWXZhjJ+/uJCjj+0ixI1EZE0F80zah2BLcBx1M2n5oC0SdRqnTI0BusOZreGK8MvryMiIiJSK1Ki1iUw4nMRdQlaLT1fLyIiIhJnkRK1TCCP0LNSKFETERERibNIidoG59xtCYtERERERBqItDKBhiGKiIiI+ChSonZ8wqIQERERkX2EvfXpnNuayECS1S//s5hVW3Y2/sTiO2Huo3XvXU3MYpLUdsLgrqy68zS/wxARkSQQzfQcae2Zj9YA0LpFJl3b5kR/4vwnoGzDvvt7FMQmMBEREUl5StSiNOP6CbRqcQDN9f03oV2vuvd5XWMXlIiIiKS0SM+oST052ZkHdmJeF2jbo+4n4wDrkbQx+4stnH7fu/zyP4v9DkVERHymHjWRJFO2u4pF67bTtU2u36GIiIjP1KMmIiIikqSUqImIiIgkKSVqIiIiIklKz6hFsLOiih0V1dGfULIUPrgfqithx5b4BSYiIiJpQYlaBDM+3RTcbpEZRefjzD/D/Mfr3mdkQU7bOEQmIiIi6UCJWgQV1Q6AAV3yaJEVRaJWXeG9jr4Yeh8J+QOhVcf4BSgpqUf7lnx7TG8O7aYkX0Qk3SlRi8Kgbm0ad0KfcTBiUnyCSWKrNu/g1YUbqK5xIY9/q7A33dp5U068vayEhetKQ5brnJfDBUf0Cb6/738rCF0jFA3KZ3iv9gAsWlfKW8tKwsb3owmHkJFhADz90Wo2bt8Tstzg7m05YbA3MXFJ2W6e+tBbnWLlygo+qV6RkM/0m3OGh/0cIiKSPpSoSczcPW05ry3cQJg8jWMG5geTmulLN/LE7NUhyx3WvW2DpOaP0z8NW2eH1i0aJGp/ePPTsPFdMeGQ4Pa/Zq9mwdrQSdWkwt7BRG1T2Z6GdX7WsP54fyYREUlvStQkZv58wSg++HwLMz/fHPJ4/bVSiwZ1oWPrFiHL5bdpuKbqjyYcErb3aVjPdsHtwT3acuVxh4QpCVZve2Jhb44ZmB+y3NB6debn5QTrXPXll/Q96KAGZeP9mUREJL0pUZOYGntwJ8Ye3Gm/5U4c3JUTB0e37ul1Jw2KqtzwXu2j7on6zpEH7b8Q0KVtLj8OXL+4eANFReFjicdnEhGR9KZ51ERERESSlBK1CEp3VUZfuKoClr0Sv2CagZ8+u4AT/vAOc7/82u9QREREUoIStQj+M389ALsro5j0dsG/oHKnt50V+jmlVLdu2y4+KymPrr1ERERkv5SoRdChdTbQ8OHysHbWW4ngkBPjFJGIiIikEyVqUTi0MfOoHXUt5OTFLxgRERFJG0rURERERJKUEjURERGRJKVETURERCRJ+TLhrZldC/wAcMBC4HvAY0DtLKDtgW3OuYIQ564CyoBqoMo5Vxj/iCUapwztxqBubegeWFJJREREmibhiZqZ9QSuAgY753aZ2TPA+c65SfXK/B4IvRCjZ4JzLvQ6RYlWsRN2fQ27t/sdie/+b2xfv0MQERFJKX4tIZUFtDSzSqAVsL72gJkZ8C3gOJ9iC1q83ku+XLhFGXduhXsLYHeknFJERETkwCT8GTXn3DrgbmA1sAEodc5Nq1fkaGCjc25FuCqAaWY218wmxzPWQV29aTnK91SFLrBttZekZWRBmx7QaQAM/EY8Q0pqSzds58OVWxu3ooOIiIiEZS5sd1GcLmjWAXgemARsA54FnnPOPR44/lfgM+fc78Oc38M5t97MugBvAlc652aEKDcZmAzQtWvX0U899VSj4iwvL+dvy7NYsKmaq0flMLLLvp2PeWWfUzj3Osry+jO38I+Nqr85KS8vJy9v/3PD/fbDXSzdWsP1h+cyuFNmAiJLrGjbIdWpHTxqhzpqC4/awaN2qDNhwoS5TX2W3o9bnycAK51zmwDM7AVgHPC4mWUB5wCjw53snFsfeC0xsxeBMcA+iZpzbiowFaCwsNAVFRU1Ksji4mI6dmwFmzYxfNgwig7rum+h9e1hLrTJy6Ox9TcnxcXFUX2+qStmwdYtjBgxgvGHdI5/YAkWbTukOrWDR+1QR23hUTt41A6x5cf0HKuBI82sVeB5tOOBpYFjJwDLnHNrQ51oZq3NrE3tNnASsChegdb2NZrF6woiIiIi4fnxjNps4DngY7ypOTII9HwB5wNP1i9vZj3M7LXA267Ae2a2APgQeNU593r8Yg3EgDI1ERERSTxfRn06524Fbg2x/+IQ+9YDpwa2vwBGxDu+4LVrN5SniYiIiA+0MkEEtQMtlKeJiIiIH5SoRcFCPaS2dSU8dELigxEREZG04deEt81C3TNqIXzxNtQE5gvrcliiQkpqvz13OLsrq+nZoaXfoYiIiKQEJWoRuMBTaiFHfdZmcf2OhbMfSFxQSax3x1Z+hyAiIpJSdOszgqhGfXbsDxlqRhEREYk9ZRgRBBM1jSaIyv1vf8a1T8/n041lfociIiKSEpSoRRC89elzHM3F+59t5sV569hUtsfvUERERFKCErUIgsugKlMTERERH2gwQQR1eVriM7Vn5qyheHlJyGPd2rbkljMGB99f/dQ8KqtrQpY9b3QvjjvUW6d01hdb+McHq8Je84+TCsjJ8hZT//205Xy+qRyAkpLdPLNubrDcmL4duXh8PwDWb9vFr19dAsDyr3TLU0QSZEq78MdOvwcKv+dtz/k7vHJNhHpK67YfPAY2LAhdbtRFcOa93vb6eTC1aJ8iRQDFwORi6DHS2/nyVfDxY6Hr7D4CLq23VHUSfqagxnymQbfViyVFPtOB/jnFgBK1SMI9o7b8dVj4XFwvvWT9dl5b+FXIY4d0yWvw/vVFX7GnKnSiVnhQx+D2uq93ha0T4A/fqtv+4PMtzPny67odG+vOyw0kcwA79lTtU2fnvJyw1xAROSAPHuO91v8HUyQNmAve30tdhYWFbs6cOY06p7i4mPuXteCjVV/z9OQjOaJ/p7qDvxsAOwK9XUddByfssxpWky1aV8qXW3aGPNY6J5OiQV2C7/+7cAM1Yf4YB/doS7/OrQFYs3Unn6wtDV0QOGVoNzIzvKx05meb+XqnN0/c4iWLGTJ4SLBczw4tKejdHoCy3ZXM+HRz8Fj39rmM6tNh/x+wGSouLqaoqMjvMHyndvCoHeokpC1qezGmhP8d5jd9JzxqhzpmNtc5V9iUOtSjFkHdqM+9utSqAg/Ln3o3DP8WsXT/25/xWUk5Pyw6mNOGd4/qnG8Mi65c746top7rbNwhnYPbrbcupyhMLG1ys6OOU0RERBpHgwkiqAlkahnhHlEbNhFyI9yrPgAaOSkiIiK1lKhFEBxMoFGfIiIi4gMlahHUPb6nTE1EREQST4laBOpRExERET9pMEEkTisTiIgkhVEX+R2BiC+UqEWwbttuIMSozz3JOzxcRCQl1U5iKpJmlKhFsLncG3mZVX/Y5556s+9nxL75BnVrQ2V1DW1y9UcjIiKS7pQNRJCdaVRWu4YrAezeXredk7fvSU106xlD9l9IRCTdrJ/nvdYu+SOSJpSoRVA7239mqInU2vRIbDAiIumsds3GJF6ZQCQelKhF4IIT3iZuOMHuymqcgxZZGaETRBEREUkbmp4jgtoetUSmS5c8+hGH3fI6s77YksCrioiISDJSohZG/cXqNY+aiIiI+EGJWhiu3vY+03OIiIiIJIAStf3QY2IiIiLiFyVqYdQ+n7bPQII3fh7YcIiIiIjEk0Z9hhF2nc+vV3mvnQcmMBoRkTQ3udjvCER8oUQtjNqxBGGfTzthSsJiERFJe5roVtKUErUwgj1qCb7uD4sO5rzRvRjQJfarHoiIiEjzokQtnHDPqMXZ0QPyE3o9EZFm4eWrvFctzi5pRoMJwqgJvGpmDhGRJPDxY96PSJpRj9p+JLpH7b8LN7C+dDenDO1Gz/YtE3ptERERSS5K1MIILh+V4B61f876kpmfb+HQbm2UqImIiKQ53foMozpw77NBnrZ2DmyY70M0IiIiko6UqIXx9R4vU9u+u6pu58Jn67bb9UpwRCIiIpJulKjtR//Oreve1E6udvRPIK+LPwGJiIhI2tAzavuRk525787W8ZlCo6RsNzM/3xKXusUnT0yEFdNCHxt1Ud1UA+vnwdSi8PVMLq6b8PPlq8KPfus+Ai6dUfd+SrvwdZ5+DxR+z9ue83d45ZrwZaeUBjdHz7kOij8PXa6ZfiYePAY2LAhdLsxnKgIo3qtsM/9MIUXxmYoAlsf5M3UfEb6MSApTopZEZn+xNbjdp2MrHyORmCnf6HcEIqmhfhIokkbMudRfXLywsNDNmTOnUec89vL/uHXmbg7r3pb/Xn20t/O16+HDB+GU38KRl8U8zjVbd/Luis3kt8nhxMFdY17/gSouLqaoqMjvMHyndvCoHTxqhzpqC4/awaN2qGNmc51zhU2pQz1qSaR3x1ZccEQfv8MQERGRJKHBBCIiIiJJSj1qjbF5eVyr/6yknOLlJfTPb81xhybPrU9pgtoHqus/5C0iIhIl9aiFsaPSe91Svqdu5+7AP7YVZXG55pIN2/n1q0t54eN1calfREREmhclamFkBpYkyMqotzZBbqB3pOvQxAckIiIiaUeJWhi1Y2F7dQgxTUZWTkJjERERkfSkRC2M4KwlCV6UXURERKSWErX9yFCiJiIiIj5RohZGXYeaMjURERHxh6bnCKP21mdGAlPZTDNaZmfSIkv5c8o4/R6/IxARkWZMiVoYLtCn1qBHbfuGuF7ztOHdOW1497heQxKsduFpERGRA6CumzBqe9SsNk+rqamb8DZD+a2IiIjEnxK1MILPqNVmaq667mDvIxIejzRTc/7u/YiIiBwAJWphhJ2dIyMrbvOovbVsI8f9vphfvbIkLvWLD165xvsRERE5AErUwggOJkjgoM/yPdV8sWkHG7fvTtxFRUREJGkpUQtjn1ufIiIiIgmmRG0/lKaJiIiIX3xJ1MzsWjNbbGaLzOxJM8s1sylmts7M5gd+Tg1z7ilmttzMPjOzG+MVY92oT6VqIiIi4o+EJ2pm1hO4Cih0zg0FMoHzA4f/6JwrCPy8FuLcTOB+4BvAYODbZjY4HnHW3fqMR+0iIiIi++fXrc8soKWZZQGtgPVRnjcG+Mw594VzrgJ4CjgrHgGuK68B6t36rNjhvQZXaxcRERGJr4Qnas65dcDdwGpgA1DqnJsWOPwjM/vEzB4xsw4hTu8JrKn3fm1gX8zlZHop2pqvd3k7Std6r/XnU4ux/p1b8/2j+jFhUJe4XUMSbEqp9yMiInIAzCW4hyiQgD0PTAK2Ac8CzwFvApvx7jr+CujunLtkr3MnAic7534QeP9dYIxz7soQ15kMTAbo2rXr6KeeeqpRcf5neTnPrzROPCiLCw/LoXX5Kg6fczXlrQ9izuH3Nqqu5q68vJy8vDy/w/Cd2sGjdvCoHeqoLTxqB4/aoc6ECRPmOucKm1KHH2shnQCsdM5tAjCzF4BxzrnHawuY2d+AV0KcuxboXe99L8LcNnXOTQWmAhQWFrqioqJGBfnGqjeBCnr16kVR0RD4ahHMgbzWeTS2ruauuLg47T5zKGoHj9rBo3aoo7bwqB08aofY8uMZtdXAkWbWyrwhlccDS82s/mrk3wQWhTj3I2CAmfUzsxZ4gxBejnvECbK5fA+zvtjCio1lfocisfLgMd6PiIjIAfDjGbXZeLc6PwYWBmKYCtxlZgvN7BNgAnAtgJn1MLPXAudWAT8C3gCWAs845xYn+jPEy8zPt3D+1Fn86X8r/A5FYmXDAu9HRETkAPhx6xPn3K3ArXvt/m6YsuuBU+u9fw3YZ+qO5qyy2hthWl1T43MkIiIikkx8SdSkzk+fXcCzc9f6F8Ccv0deNLz+iMUHjwnfOzTqIjgzMMhi/TyYWhS+zsnF0GOkt/3yVfDxY6HLdR8Bl86oF0u78HWefg8Ufs/bjtNnKio+C4rD1BntZxIREWkEJWphbN5Vr3dr22r416S4XKd9q+zgdlaGkZ2ZwbED8+NyLfHJgJP8jkBERJopJWphtMry5lFbvWUnLH8Ptgd6vdr3jnBW49102mBuOi0uiytEp/B7dT1R+1O/dyuSHiOjnzvszHvreq32J9o64/SZioteim4kU2M+k4iISARalD2MjMCSBId2bwM1Vd6bHqNg4qO+xRRzc/7u/YiIiEhSUo/afmSY1S0b1edIyG4Z0/o/3VhGZXUNA7q0oUVWgvPm2ue4ou19EhERkYRSj1pUgku0x7zmix75kNPufY/N5XtiXreIiIg0b0rUwmiQmtX2qFnsEzURERGRcJSo7Y8ZdWmbiIiISOIoUQujwVr16lETERERHyhR2w8vNYvfM2oiIiIi4ShR248GnWjqURMREZEE0vQcYTR4Ks2l6DNq0U4gKyIiIr5QohZG3c1OI563Ph+6qJDKakenvBYxr1tERESaNyVq++EN+ozfYIIhPSIsNC4iIiJpTc+oheNCvUmxZ9QePMb7ERERkaSkHrUwGk54W/sm9onaXa8vo3RXJT89eRDtWyX49ueGBYm9noiIiDSKetT2wxpmajGv/8V563hi9mp2VlTHvG4RERFp3pSoRaO6wnvV9BwiIiKSQErUwqgbP2Cw5CXvTU2VfwGJiIhI2lGiFo32B3mvrTr7G4eIiIikFSVqYYSc4rbzwESHISIiImlMoz73I6UfSxt1kd8RiIiISARK1PbD4jx32iFd8mjfqgVZmT5khGfem/hrioiISNSUqIWRqNU9//n9IxJ0JREREWlu9IxaGHFcNSp5rJ/n/YiIiEhSUo/afqRynsbUIu91SqmvYYiIiEhoStTCctyY9S9+UPw6uPjNnzb+zrfYULqL9244jh7tW8btOiIiItL86NZnGA44MWMumbVJWut86Dok5tepcY6aRD0QJyIiIs2KetSi8cMPIP9QyIh9XruhdHfM6xQREZHUoB61cBxY7djPzOy4JGnTFn8V3M5I5KiF9fNgSrvEXU9EREQOiBK1MBz1ErU4DSlYvXVncLtr25y4XGO/Bpzkz3VFRERkv3TrM4Jgehbn3q7vH9XPW/w9UXqM1EhPERGRZkA9amE07FGLj7a52fTr3JqOrVvE9ToNvHyV9yMiIiJJTz1qEQQTtTj1dn3r8N586/Decak7rI8f8161fJSIiEjSU49aGBvKXb0n01J62lsRERFJUkrUwmiTA2bpsI6UiIiIJCslamFkNkjO4pOoPfr+Sobc8jq/fX1ZXOoXERGR5k2JWhiu/jiCOPWoVdU4dlRUU1FVE5f6RUREpHlTohZBvOdRExEREYlEoz7DqMHFfXoOX3Qf4XcEIiIiEiUlauG4xE14m1CXzvA7AhEREYmSbn2GkYglpEREREQiUaIWhnPQzb723qRSj5qIiIg0G7r1GYa56ro3WblxucaYfh35+amHMrRnu7jUH9KUwLW01qeIiEjSU6IWhlFvyoxWHeNyjeG92jO8V/u41C0iIiLNn259hlE7j1p1Rra/gYiIiEjaUo+ajz4rKWfx+lIOzs9L7O1PERERaRbUoxZGIsZ7Fi8v4eqn5vPivHVxvIqIiIg0V0rUwnApONetiIiINC9K1MJQniYiIiJ+0zNqYVSm6jrpp9/jdwQiIiISJSVqYeR+vQxaAC7FMrbC7/kdgYiIiERJtz7DODRnKwAZ9Se+FREREUkgJWr7UTbwHL9DiK05f/d+REREJOnp1qeP/m9sX84f04esjASuJfrKNd6rboGKiIgkPSVqYQTnUYvjguwtsjJokaVOTREREQlNWYKIiIhIklKPWhjRTHi7u7Ka/yxYH/b4kf070btjKwCWbtjOonWlDY6/unADy78q4/Kig/nu2L5NCVdERERSkC+JmpldC/wA7w7jQuB7wK+AM4AK4HPge865bSHOXQWUAdVAlXOuMDFR72tXRTU/fe6TsMf/euGoYKL21rISfvfG8pDl1pfuPvAgXr4KPn4s9LHuI+DSGXXvp2g9URERkeYk4YmamfUErgIGO+d2mdkzwPnAm8DPnHNVZvZb4GfADWGqmeCc2xzPOAtZ7MUboUyLrAzOHdUr7PGeHVoGtw/t1iZk2ZYtMrhgTJ/GBbd+nvfaY2Tjzqs14KQDO09EREQSyq9bn1lASzOrBFoB651z0+odnwWc50tkAdWBx/cy9pSGLdM6J4vff2tEVPUdf1hXjj+sa0xiY2qR9zqlFM681/uJxpTwn0VERPxXWVnJ2rVr2b27CXdafNauXTuWLl3qdxgJlZubS69evcjOzo553QlP1Jxz68zsbmA1sAuYtleSBnAJ8HS4KoBpZuaAB51zU+MRZ21P2q7+J9MyxPHdldV8sraUnKwMRvRuH48QREQkzaxdu5Y2bdrQt2/fuM46EE9lZWW0adPG7zASxjnHli1bWLt2Lf369Yt5/X7c+uwAnAX0A7YBz5rZd5xzjweO3wRUAU+EqWK8c269mXUB3jSzZc65GXsXMrPJwGSArl27Ulxc3LhAXQ0YfPHFSj6p2ffcjTtquOHdXXRpZdx1TKvG1d1ERYHXRn+mA1ReXp6wayUztYNH7eBRO9RRW3hi0Q7t2rWjU6dOlJeXxyYoH1RXV1NWVuZ3GAnVokULtm3bFpe/B37c+jwBWOmc2wRgZi8A44DHzewi4HTgeOdCj7t0zq0PvJaY2YvAGGCfRC3Q0zYVoLCw0BUVFTUqyJeK/wBA/4MPpuPR+567avMOeLeYli1b0ti6m6zYe0nUdYuLixP/GZOQ2sGjdvCoHeqoLTyxaIelS5fStm3b2ATkk3TrUauVm5vLyJEH+Ox4BH7Mo7YaONLMWpnXr3s8sNTMTsEbPHCmc25nqBPNrLWZtandBk4CFsUjyNoOZ0vkqgEiIiI+2rJlCwUFBRQUFNCtWzd69uwZfF9RURHx3Dlz5nDVVVc16np9+/Zl2LBhFBQUMGzYMF566aWmhL+PKVOmcPfddwNwyy23MH369JjWnwh+PKM228yeAz7Gu8U5D6/nazGQg3c7E2CWc+4yM+sBPOScOxXoCrwYOJ4F/Ms593qcIg28KlETEZH00KlTJ+bPnw94SU5eXh4/+clPgserqqrIygqdOhQWFlJYWNjo255vv/02nTt3Zvny5Zx00kmcddZZBxx/JLfddltc6o03X1YmcM7d6pw71Dk31Dn3XefcHufcIc653s65gsDPZYGy6wNJGs65L5xzIwI/Q5xzt8crRnNK1ERERC6++GKuu+46JkyYwA033MCHH37IuHHjGDlyJOPGjWP5cm+O0OLiYk4//XTAS/IuueQSioqK6N+/P/feu//ZCbZv306HDh2C788++2xGjx7NkCFDmDrVGzdYXV3NxRdfzNChQxk2bBh//OMfAfj888855ZRTGD16NEcffTTLli0L+Tmee+45wOvJu/XWWxk1ahTDhg0Llt+xYweXXHIJhx9+OCNHjox5D9+B0MoE+5GUo24mF/sdgYiIxFnfG1+NS72r7jyt0ed8+umnTJ8+nczMTLZv386MGTPIyspi+vTp/PznP+f555/f55xly5bx9ttvU1ZWxqBBg/jhD38YcvqKCRMm4Jzjiy++4Jlnngnuf+SRR+jYsSO7du3i8MMP59xzz2XVqlWsW7eORYu8p562bdsGwOTJk3nggQcYMGAAs2fP5vLLL+ett96K+Jk6d+7Mxx9/zF/+8hfuvvtuHnroIW6//XaOO+44HnnkEbZt28aYMWM44YQTaN26daPbLFaUqIVhtbc+LQmXQz3QiW5FREQOwMSJE8nMzASgtLSUiy66iBUrVmBmVFZWhjzntNNOIycnh5ycHLp06cLGjRvp1Wvfid9rb31+/vnnHH/88RQVFZGXl8e9997Liy++CMCaNWtYsWIFgwYN4osvvuDKK6/ktNNO46STTqK8vJyZM2cyceLEYJ179uzZ72c655xzABg9ejQvvPACANOmTePll18OPte2e/duVq9ezWGHHdaI1ootJWph1CZq4XrUerRvyfTrjiErIwkTORERafYOpOcrXur3KN18881MmDCBF198kVWrVoUd6ZqTkxPczszMpKqqKuI1Dj74YLp27cqSJUvYuXMn06dP54MPPqBVq1YUFRWxe/duOnTowIIFC3jjjTe4//77eeaZZ7jnnnto37598Nm6aNXGVz825xzPP/88gwYNalRd8aQsY79CJ2otsjI4pEsb+nb2oTv05au8HxERkQQrLS2lZ8+eADz66KMxq7ekpISVK1dy0EEHUVpaSocOHWjVqhXLli1j1qxZAGzevJmamhrOPfdcfvWrX/Hxxx/Ttm1b+vXrx7PPPgt4ydaCBQsOKIaTTz6Z++67j9oZwubNmxebD9cEStTCqLv1mYTPqH38WPiF2EVEROLo+uuv52c/+xnjx4+nurq6yfVNmDCBgoICJkyYwJ133knXrl055ZRTqKqqYvjw4dx8880ceeSRAKxbt46ioiIKCgq4+OKL+c1vfgPAE088wcMPP8yIESMYMmTIAQ8CuPnmm6msrGT48OEMHTqUm2++ucmfr6l06zMMoyawETpRKynbzR2vLqVzXg6/OH1wAiMTERGJvylTpoTcP3bsWD799NPg+1/96leANwl7UVERZWVl+5xb+/D/3latWhVyf05ODv/9739DHvv444/32devXz9ef33f2brqx1G/96/+dQsLC4MrCrRs2ZIHH3ww5HX9oh61MIIT3oYZTLBzTzX/nr+eN5duTFxQIiIiklaUqIWR1Lc+RUREJC0oUQujr1sX2FKiJiIiIv5QohbGoIy1AMF5Y0REREQSTYMJwtji2tDJymjZd4zfoeyr+wi/IxAREZEEUKK2H5ad63cI+7p0ht8RiIiISALo1ucBysnO4PC+HRjWs53foYiIiMTEli1bKCgooKCggG7dutGzZ8/g+4qKiv2eX1xczOzZs0Mee/TRR8nPz6egoIAhQ4Zw3nnnsXPnzpjGn5eXB8D69es577zzYlq3X5SohVA7IzGEX0Kqe7uWPHvZOP58wahEhSUiIhJXnTp1Yv78+cyfP5/LLruMa6+9Nvi+RYsW+z0/UqIGMGnSJObPn8/ixYtp0aIFTz/9dCzDD+rRowfPPfdcXOpONCVqIdTL05LTlHbej4iISJzNnTuXY489ltGjR3PyySezYcMGAO69914GDx7M8OHDOf/881m1ahUPPPAA999/PwUFBbz77rth66yqqmLHjh106NABgP/85z8cccQRjBw5khNOOIGNG705St95551gj97IkSMpKysD4He/+x2HH344w4cP59Zbb92n/lWrVjF06FDA68k755xzOOWUUxgwYADXX399sNy0adMYO3Yso0aNYuLEiZSXl8em0WJIz6gdoJoax56qGswgN1sjQ0VEJPb63vhq2GN3fHMYFxzRB4B/zV7Nz19cGLbsgS7w7pzjyiuv5KWXXiI/P5+nn36am266iUceeYQ777yTlStXkpOTw7Zt22jfvj2XXXYZ2dnZ3HTTTSHre/rpp3nvvffYsGEDAwcO5IwzzgDgqKOOYtasWZgZDz30EHfddRe///3vufvuu7n//vsZP3485eXl5ObmMm3aNFasWMGHH36Ic44zzzyTGTNmcMwxx4T9HPPnz2fevHnk5OQwaNAgrrzySlq2bMmvf/1rpk+fTuvWrfntb3/LH/7wB2655ZYDaqt4UaIWggOyibx+2eqtOym6u5iDOrXinZ9OSExgIiIiCbRnzx4WLVrEiSeeCEB1dTXdu3cHYPjw4Vx44YWcffbZnH322VHVN2nSJP785z/jnOOKK67gd7/7HTfeeCNr165l0qRJbNiwgYqKCvr16wfA+PHjue6667jwwgs555xz6NWrF9OmTWPatGmMHDkSgPLyclasWBExUTv++ONp1867EzV48GC+/PJLtm3bxpIlSxg/fjwAFRUVjB079oDaKZ6UqIXgnKOt7ax9428wIiKStqLtCbvgiD7B3rVYcs4xZMgQPvjgg32Ovfrqq8yYMYOXX36ZX/3qVyxevDjqes2MM844g/vuu48bb7yRK6+8kuuuu44zzzyT4uLi4BqdN954I6eddhqvvfYaRx55JNOnT8c5x89+9jMuvfTSqK+Xk5MT3M7MzKSqqgrnHCeeeCJPPvlk1PX4Qc+oheCAcheYliMrJ2JZERGRVJWTk8OmTZuCiVplZSWLFy+mpqaGNWvWMGHCBO666y62bdtGeXk5bdq0CT5Htj/vvfceBx98MAClpaX07NkTgMceeyxY5vPPP2fYsGHccMMNFBYWsmzZMk4++WQeeeSR4PNk69ato6SkpNGf7cgjj+T999/ns88+A2Dnzp0NFptPFupR25+9Rn3uWT2XnEeOoy+wKhfYAUypV2ByMfTwumN5+Sr4+DFC6j6i4XxokQYHnH4PFH7P257z90YELyIicuAyMjJ47rnnuOqqqygtLaWqqoprrrmGgQMH8p3vfIfS0lKcc1x77bW0b9+eM844g3POOYfXX3+d++67j6OPPrpBfbXPqNXU1NCrVy8effRRAKZMmcLEiRPp2bMnRx55JCtXrgTgnnvu4e233yYzM5PBgwfzjW98g5ycHJYuXRq8TZmXl8fjjz9Oly5dGvXZ8vPzefTRR/n2t7/Nnj17APj1r3/NwIEDm9hqsaVELQTn6i3KDnz1+GQc0OWCB6muSZJboQNO8jsCERFJYbW3HwFmzNh3ovX33ntvn30DBw7kgw8+oE2bNvscu/jii7n44otDXuuss87irLPO2mf/fffdF7L81VdfzdVXX73P/tpetr59+7Jo0aKQ133llVeC28cddxwfffRRyGskCyVqITjqJ2NGt8+8eV52V/+VrF6j+Ntx87wjBkWD8jmky75fSADOvNf7icaU0ujKFX6vrndNREREUpoStRAijR9okZXB/zumf+KCERERkbSlwQQiIiIiSUqJWhihF44SERERSRwlaiE0uPUZZq1PERERkXhTohZCw8EEIiIiIv5QohbC3tNzLHL9WFjT17+AREREEmDLli3BRdC7detGz549g+8rKioinjtnzhyuuuqqqK/1pz/9iWuuuSb4/tJLL+WEE04Ivr/vvvsi1vfAAw/wj3/8I+I1Hn30UX70ox+FPHbHHXdEHWs09cWLRn3ul9Hu6pk4By0yldeKiEjq6tSpE/Pnzwe8edTy8vL4yU9+EjxeVVVFVlbo1KGwsJDCwsKoVyYYN24cTzzxRPD9/Pnzqampobq6mszMTGbOnBlxDdHLLrssquuEc8cdd/Dzn/+8SXUkgjKPEPa+8dm7Yyv6dGpFRoaeVxMRkfRy8cUXc9111zFhwgRuuOEGPvzwQ8aNG8fIkSMZN24cy5cvB6C4uJjTTz8d8JK8Sy65hKKiIvr378+99+47p+jIkSP59NNP2bVrF6WlpbRq1YqCggIWLlwIwMyZMxk3bhyff/45p5xyCqNHj+boo49m2bJlwWvcfffdAHz00UcMHz6csWPH8tOf/pShQ4cGr7N+/XpOOeUUBgwYwPXXXw94a4ju2rWLgoICLrzwQgAef/xxxowZQ0FBAZdeeinV1dUA/P3vf2fgwIEce+yxvP/++/Fo4ojUoxaCc67BrU8REZGEi7S0YJPqjXKC9Xo+/fRTpk+fTmZmJtu3b2fGjBlkZWUxffp0fv7zn/P888/vc86yZct4++23KSsrY9CgQfzwhz8kOzs7eDwrK4uCggI++ugjdu3axRFHHMGAAQOYOXMmXbp0wTlH7969Of7443nggQcYMGAAs2fP5vLLL+ett95qcK3vfe97TJ06lXHjxnHjjTc2ODZ//nzmzZtHTk4OgwYN4sorr+TOO+/kz3/+c7D3cOnSpTz99NO8//77ZGdnc/nll/PEE09w4okncuuttzJ37lzatWvHhAkTGDlyZKPbrymUqIXQIEUzC/5lqfjF17TIUiekiIikl4kTJ5KZmQl4C6hfdNFFrFixAjOjsrIy5DmnnXYaOTk55OTk0KVLFzZu3EivXr0alBk/fjwzZ85k165djB07lgEDBnDHHXeQn5/PuHHjKC8vZ+bMmUycODF4Tu26nLW2bdtGWVkZ48aNA+CCCy5osEzU8ccfT7t23r/jgwcP5ssvv6R3794N6vjf//7H3LlzOfzwwwHYtWsXXbp0Yfbs2RQVFZGfnw/ApEmTEr5wuxK1RqiJtGSBiIhILB1Az1e8tG7dOrh98803M2HCBF588UVWrVpFUVFRyHNycnKC25mZmVRVVe1TZty4cTz44IPs3r2bK664gvz8fJYsWUJ+fj7jx4+npqaG9u3bB3u+QnH7+bc5mjicc1x00UX85je/abD/3//+N+bzNF3qHgrBG/UpIiIieystLaVnz56ANwqyKcaNG8esWbPYtGkTXbp0wczIz8/npZdeYty4cbRt25Z+/frx7LPPAl5CtWDBggZ1dOjQgTZt2jBr1iwAnnrqqaiunZ2dHewNPP7443nuuecoKSkBYOvWrXz55ZccccQRFBcXs2XLFiorK4NxJJIStRC27qg/BFkpm4iISK3rr7+en/3sZ4wfPz74wP2B6tChA/n5+QwZMiS4b+zYsZSUlDBixAgAnnjiCR5++GFGjBjBkCFDeOmll/ap5+GHH2by5MmMHTsW51zwVmckkydPZvjw4Vx44YUMHjyYX//615x00kkMHz6cE088kQ0bNtC9e3emTJnC2LFjOeGEExg1alSTPu+BsP11GaaCwsJCN2fOnKjLf76pnF5/7kuOVcJNG+H2rgDsvmkrudmZ8QozaRUXF4ft2k4nageP2sGjdqijtvDEoh2WLl3KYYcdFpuAfFJWVkabNm0Ses3y8nLy8vIAuPPOO9mwYQN/+tOfEhpDqD87M5vrnCtsSr16Ri2s1E9gRUREUsGrr77Kb37zG6qqqjjooIOafEs2mShRC0FrfYqIiDQfkyZNYtKkSX6HERdK1KLw947XUFPj+K6SNhEREUkgJWph1J/w9ntX/dLHSERERCRdadRnSA3uffoWhYiIiKQ39ahFYefMhwBoOfb7vk98JyIiIulDPWph1E/HWk37Ma2m/Zg9VTW+xSMiIhJvW7ZsoaCggIKCArp160bPnj2D7ysqKvZ7fnFxMbNnz95nv3OOzp078/XXXwOwYcMGzIz33nsvWCY/P58tW7aErbt2iahI+vbty+bNm0PGNXPmzP2eH219iaRELQSN+hQRkXTUqVMn5s+fz/z587nsssu49tprg+9btGix3/PDJWpmxhFHHMEHH3wAwMyZMxk5cmQweVq+fDmdO3emU6dOYes+kESrflxNOd9PStREREQkrLlz53LssccyevRoTj75ZDZs2ADAvffey+DBgxk+fDjnn38+q1at4oEHHuD++++noKCAd999t0E9tQuwg5d0XXfddQ0St9oes9/97nccfvjhDB8+nFtvvTV4fu2EtjU1NVx++eUMGTKE008/nVNPPZXnnnsuWO6+++5j1KhRDBs2jGXLlgXj+uMf/xiMa9OmTZx77rkcfvjhHH744bz//vuA16N40kknMXLkSC699NL9riOaCHpGLQzThLciIuK3KRGWQjr9Hij8nrc95+/wyjUR6jmwBd6dc1x55ZW89NJL5Ofn8/TTT3PTTTfxyCOPcOedd7Jy5UpycnLYtm0b7du357LLLiM7O5ubbrppn7rGjRvHbbfdBsCHH37IL3/5S+655x7AS9TGjx/PtGnTWLFiBR9++CHOOc4880xmzJjBMcccE6znhRdeYNWqVSxcuJCSkhIOO+wwLrnkkuDxzp078/HHH/OXv/yFu+++m4ceeojLLruMvLw8fvKTnwBwwQUXcO2113LUUUexevVqTj75ZJYuXcovf/lLjjrqKG655RZeffVVpk6dekDtFktK1EJomKLp1qeIiKSnPXv2sGjRIk488UQAqqur6d69O0Bwncyzzz6bs88+e791jRkzhnnz5rFjxw4qKyvJy8ujf//+fPbZZ8ycOZMf//jHPPTQQ0ybNo2RI0cC3tJQK1asaJCovffee0ycOJGMjAy6devGhAkTGlznnHPOAWD06NG88MILIWOZPn06S5YsCb7fvn07ZWVlzJgxI3jOaaedRocOHaJsqfhRoiYiIpKsou0JK/xeXe9aDDnnGDJkSPAWZX2vvvoqM2bM4OWXX+ZXv/oVixcvjlhXq1atOOSQQ3jkkUeCi5sfeeSRvPbaa5SUlDBo0CCcc/zsZz/j0ksvjRhTJDk5OQBkZmZSVVUVskxNTQ0ffPABLVu23OdYss3uoGfUwtCtTxERSXc5OTls2rQpmKhVVlayePFiampqWLNmDRMmTOCuu+5i27ZtlJeX06ZNG8rKysLWN378eO655x7Gjh0LwNixY/nTn/7EkUceiZlx8skn88gjj1BeXg7AunXrKCkpaVDHUUcdxfPPP09NTQ0bN26kuLh4v59j77hOOukk/vznPwffz58/H4BjjjmGJ554AoD//ve/wVGqflKiFsLeoz5Lr9/M6MznyM5Uc4mISPrIyMjgueee44YbbmDEiBEUFBQwc+ZMqqur+c53vsOwYcMYOXIk1157Le3bt+eMM87glVdeCTmYALxE7YsvvggmaqNGjWLt2rXBgQQnnXQSF1xwAWPHjmXYsGGcd955+yR+5557Lr169WLo0KFceumlHHHEEbRrF+FZPuCMM87gxRdfDMZ17733MmfOHIYPH87gwYN54IEHALj11luZMWMGo0aNYtq0afTp0ycWzdgklgwjGuKtsLDQzZkzJ+ryy78q45C/9ibTHNyylRoy2Fy+hy5tc+MYZfIqLi6mqKjI7zB8p3bwqB08aoc6agtPLNph6dKlHHbYYbEJyCdlZWW0adMmrtcoLy8nLy+PLVu2MGbMGN5//326desW12vuT6g/OzOb65wrbEq9ekYtjNo71Jc9PoceHdpwyxmDfY1HREREPKeffjrbtm2joqKCm2++2fckLZ6UqIXg6j2f9saSEqa1/iGsz4NLZ/gYlYiIiABRPZeWKpSoRWFA9eewwe8oREREJN3o6fgwMiz1n90TEZHkkw7PjqeaeP6ZKVHbD6cJb0VEJEFyc3PZsmWLkrVmxDnHli1byM2Nz4BD3foMQX8/RETED7169WLt2rVs2rTJ71AO2O7du+OWtCSr3NxcevXqFZe6fUnUzOxa4Ad4qzUtBL4HtAKeBvoCq4BvOef2mWnOzE4B/gRkAg855+5MTNQiIiLxlZ2dTb9+/fwOo0mKi4uDS0BJ0yX81qeZ9QSuAgqdc0PxEq7zgRuB/znnBgD/C7zf+9xM4H7gG8Bg4NtmFvN5M74q3R3cPmZgl1hXLyIiIhIVv55RywJamlkWXk/aeuAs4LHA8ceAs0OcNwb4zDn3hXOuAngqcF5MbSrfE9z+xyVjYNRF3o+IiIhIAiX81qdzbp2Z3Q2sBnYB05xz08ysq3NuQ6DMBjML1ZXVE1hT7/1a4IhYx5ix94KsZ94b60uIiIiI7FfCEzUz64DXC9YP2AY8a2bfifb0EPtCPvpvZpOByYG35Wa2vJGhdgY280uN+qS2LUTt4FE7eNQOddQWHrWDR+1QZ1BTK/BjMMEJwErn3CYAM3sBGAdsNLPugd607kBJiHPXAr3rve+Fd9t0H865qcDUAw3SzOY0dX2uVKG28KgdPGoHj9qhjtrCo3bwqB3qmFn0C42H4cczaquBI82slZkZcDywFHgZqH0Q7CLgpRDnfgQMMLN+ZtYCbxDCywmIWURERCTh/HhGbbaZPQd8DFQB8/B6vvKAZ8zs+3jJ3EQAM+uBNw3Hqc65KjP7EfAG3mjRR5xzixP9GUREREQSwZd51JxztwK37rV7D17v2t5l1wOn1nv/GvBaXAP0HPBt0xSktvCoHTxqB4/aoY7awqN28Kgd6jS5LUzLVIiIiIgkJ631KSIiIpKk0j5RM7NTzGy5mX1mZqFWQzAzuzdw/BMzG+VHnPFkZr3N7G0zW2pmi83s6hBlisys1MzmB35u8SPWRDCzVWa2MPA59xmxkybfiUH1/qznm9l2M7tmrzIp+Z0ws0fMrMTMFtXb19HM3jSzFYHXDmHOjfj7pLkJ0xa/M7Nlge/+i2bWPsy5Ef8eNSdh2mGKma2r9/0/Ncy5KfOdCNMOT9drg1VmNj/Muan0fQj5b2bcfk8459L2B29AwudAf6AFsAAYvFeZU4H/4s3hdiQw2++449AO3YFRge02wKch2qEIeMXvWBPUHquAzhGOp/x3Yq/Pmwl8BRyUDt8J4BhgFLCo3r67gBsD2zcCvw3TThF/nzS3nzBtcRKQFdj+bai2CByL+PeoOf2EaYcpwE/2c15KfSdCtcNex38P3JIG34eQ/2bG6/dEuveoRbMk1VnAP5xnFtDevHneUoZzboNz7uPAdhnedCk9/Y0qqaX8d2IvxwOfO+e+9DuQRHDOzQC27rU7aZa4S6RQbeGcm+acqwq8nYU3n2VKC/OdiEZKfScitUNguq1vAU8mNCgfRPg3My6/J9I9UQu1JNXeCUo0ZVKGmfUFRgKzQxwea2YLzOy/ZjYksZEllAOmmdlc81a42FtafSfw5isM98s3Xb4TDZa4A6Jd4i6VvxcAl+D1Loeyv79HqeBHgVvAj4S5zZVO34mjgY3OuRVhjqfk92GvfzPj8nsi3RO1aJakinrZqubOzPKA54FrnHPb9zr8Md6trxHAfcC/ExxeIo13zo0CvgFcYWbH7HU8nb4TLYAzgWdDHE6n70Q00uZ7AWBmN+HNhflEmCL7+3vU3P0VOBgoADbg3fbbWzp9J75N5N60lPs+7OffzLCnhdgX8TuR7olaNEtSRb1sVXNmZtl4X7gnnHMv7H3cObfdOVce2H4NyDazzgkOMyGcN3cfzrkS4EW8rur60uI7EfAN4GPn3Ma9D6TTd4LAEncAFoMl7po7M7sIOB240AUevNlbFH+PmjXn3EbnXLVzrgb4G6E/X1p8J8wsCzgHeDpcmVT7PoT5NzMuvyfSPVGLZkmql4H/C4z0OxIore3aTBWBZwseBpY65/4Qpky3QDnMbAzed2dL4qJMDDNrbWZtarfxHpxetFexlP9O1BP2f8np8p0I0BJ3AWZ2CnADcKZzbmeYMtH8PWrW9nou9ZuE/nxp8Z3AW8N7mXNubaiDqfZ9iPBvZnx+T/g9esLvH7wRfJ/ijcK4KbDvMuCywLYB9weOLwQK/Y45Dm1wFF7X6yfA/MDPqXu1w4+AxXgjVGYB4/yOO05t0T/wGRcEPm9aficCn7MVXuLVrt6+lP9O4CWmG4BKvP/9fh/oBPwPWBF47Rgo2wN4rd65+/w+ac4/YdriM7xnbGp/Vzywd1uE+3vUXH/CtMM/A3//P8H7h7Z7qn8nQrVDYP+jtb8X6pVN5e9DuH8z4/J7QisTiIiIiCSpdL/1KSIiIpK0lKiJiIiIJCklaiIiIiJJSomaiIiISJJSoiYiIiKSpJSoiUhCmFm1mc2v99M3QtnyBIYWlpn1MLPnAtsFZnZqvWNnmtmNcbpukZmVmtlrgfeDAkvvLDCzsYF9WWY23cxa1TvvCTPbambnxSMuEUk8Tc8hIglhZuXOubxYl00UM7sYb868HyXgWkXAT5xzpwfe/wFvTc1VwJ3OuXPN7Epgu3Pusb3OfRR4xTn3XLzjFJH4U4+aiPjCzPLM7H9m9rGZLTSzs0KU6W5mMwI9cIvM7OjA/pPM7IPAuc8G1tzb+9xiM7vHzGYGzh0T2N/RzP4dWEx7lpkND+w/tl5v3zwza2NmfQPntgBuAyYFjk8ys4vN7M9m1s7MVplZRqCeVma2xsyyzexgM3s90Bv2rpkdGigzMVDvAjObEUVzVQIt8SYhrjSz9sAZwD8OoOlFpBnJ8jsAEUkbLc1sfmB7JTAR+KZzbrt5a4TOMrOXXcNu/guAN5xzt5tZJtAqUPYXwAnOuR1mdgNwHV4itbfWzrlx5i0A/QgwFPglMM85d7aZHYeX7BQAPwGucM69H0j8dtdW4pyrMLNbqNejFuhhwzlXamYLgGOBt/ESqDecc5VmNhVvxvYVZnYE8BfgOOAW4GTn3LpA0rU/9wfizAEuDZx/u9MtEZGUp0RNRBJll3OuoPaNeYsa3xFIomqAnkBX4Kt653wEPBIo+2/n3HwzOxYYDLzvLblHC+CDMNd8EsA5N8PM2gaSoqOAcwP73zKzTmbWDngf+IOZPQG84JxbG6g/Gk8Dk/AStfOBvwSSvXHAs/XqyQm8vg88ambPAC+wH8651UARgJkdgrckzTIz+2fg89/snPs02mBFpPlQoiYifrkQyAdGB3qfVgG59QsEEqxjgNOAf5rZ74CvgTedc9+O4hp79zg5vLVa9ynnnLvTzF7FW4dvlpmdQL1etf14GfiNmXUERgNvAa2BbfWT03oXuyzQw3YaMN/MCpxz0S5ofztej+JVwBN4z63diteeIpJi9IyaiPilHVASSNImAAftXcDMDgqU+RvwMDAKbwH48YGepdpnwgaGucakQJmjgFLnXCkwg0BSE3hof3Pg9uvBzrmFzrnfAnOAQ/eqqwxoE+oizrly4EPgT3gP8lc757YDK81sYuBaZmYjAtsHO+dmO+duATYDvffXWIHzjgXWOedW4D2vVgNUB7ZFJAWpR01E/PIE8B8zmwPMB5aFKFME/NTMKoFy4P+cc5sCz4c9aWa1txJ/AYS69fe1mc0E2gKXBPZNAf5uZp8AO4GLAvuvCSSM1cASvFGW3evV9TZwY+A5u9+EuNbTwLOBmGtdCPzVzH4BZANPAQuA35nZALzevf8F9kVk3v3TXwDfCuyaiteGWcAP93e+iDRPmp5DRFKSmRXjTXExx+9YGmvv6Tkaee6jaHoOkZShW58iIsmnAhhqgQlvoxUYCHEs0T9bJyJJTj1qIiIiIklKPWoiIiIiSUqJmoiIiEiSUqImIiIikqSUqImIiIgkKSVqIiIiIklKiZqIiIhIkvr/hwEktgzx39MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_0krS8g1OTbD"
   },
   "source": [
    "### Plot the AUPRC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "7jHnmVebOWOC"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f338457a8d0>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5ysRtr6xHnXP"
   },
   "source": [
    "## Oversampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "18VUHNc-UF5w"
   },
   "source": [
    "### Oversample the minority class\n",
    "\n",
    "A related approach would be to resample the dataset by oversampling the minority class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "sHirNp6u7OWp"
   },
   "outputs": [],
   "source": [
    "pos_features = train_features[bool_train_labels]\n",
    "neg_features = train_features[~bool_train_labels]\n",
    "\n",
    "pos_labels = train_labels[bool_train_labels]\n",
    "neg_labels = train_labels[~bool_train_labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WgBVbX7P7QrL"
   },
   "source": [
    "#### Using NumPy\n",
    "\n",
    "You can balance the dataset manually by choosing the right number of random \n",
    "indices from the positive examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "BUzGjSkwqT88"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181955, 29)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ids = np.arange(len(pos_features))\n",
    "choices = np.random.choice(ids, len(neg_features))\n",
    "\n",
    "res_pos_features = pos_features[choices]\n",
    "res_pos_labels = pos_labels[choices]\n",
    "\n",
    "res_pos_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "7ie_FFet6cep"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(363910, 29)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_features = np.concatenate([res_pos_features, neg_features], axis=0)\n",
    "resampled_labels = np.concatenate([res_pos_labels, neg_labels], axis=0)\n",
    "\n",
    "order = np.arange(len(resampled_labels))\n",
    "np.random.shuffle(order)\n",
    "resampled_features = resampled_features[order]\n",
    "resampled_labels = resampled_labels[order]\n",
    "\n",
    "resampled_features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IYfJe2Kc-FAz"
   },
   "source": [
    "#### Using `tf.data`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "usyixaST8v5P"
   },
   "source": [
    "If you're using `tf.data` the easiest way to produce balanced examples is to start with a `positive` and a `negative` dataset, and merge them. See [the tf.data guide](../../guide/data.ipynb) for more examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "yF4OZ-rI6xb6"
   },
   "outputs": [],
   "source": [
    "BUFFER_SIZE = 100000\n",
    "\n",
    "def make_ds(features, labels):\n",
    "  ds = tf.data.Dataset.from_tensor_slices((features, labels))#.cache()\n",
    "  ds = ds.shuffle(BUFFER_SIZE).repeat()\n",
    "  return ds\n",
    "\n",
    "pos_ds = make_ds(pos_features, pos_labels)\n",
    "neg_ds = make_ds(neg_features, neg_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RNQUx-OA-oJc"
   },
   "source": [
    "Each dataset provides `(feature, label)` pairs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "llXc9rNH7Fbz"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features:\n",
      " [-3.83382975 -0.22097711 -2.51753261  1.75784158  1.80103123  1.00122873\n",
      " -5.         -5.          0.42450587 -0.36179529  1.03479001  0.97669174\n",
      " -1.45932385 -1.49016266  0.63184127  0.95459192  3.36383192  1.93108495\n",
      "  3.1264519  -0.62139855 -5.          3.09926628  2.73269304  0.23206456\n",
      "  0.31165321  2.50727821  3.12379405  0.30096484 -0.21271977]\n",
      "\n",
      "Label:  1\n"
     ]
    }
   ],
   "source": [
    "for features, label in pos_ds.take(1):\n",
    "  print(\"Features:\\n\", features.numpy())\n",
    "  print()\n",
    "  print(\"Label: \", label.numpy())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sLEfjZO0-vbN"
   },
   "source": [
    "Merge the two together using `experimental.sample_from_datasets`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "e7w9UQPT9wzE"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/conda/lib/python3.7/site-packages/tensorflow/python/data/experimental/ops/interleave_ops.py:260: RandomDataset.__init__ (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.data.Dataset.random(...)`.\n"
     ]
    }
   ],
   "source": [
    "resampled_ds = tf.data.experimental.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n",
    "resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "EWXARdTdAuQK"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.50341796875\n"
     ]
    }
   ],
   "source": [
    "for features, label in resampled_ds.take(1):\n",
    "  print(label.numpy().mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "irgqf3YxAyN0"
   },
   "source": [
    "To use this dataset, you'll need the number of steps per epoch.\n",
    "\n",
    "The definition of \"epoch\" in this case is less clear. Say it's the number of batches required to see each negative example once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "xH-7K46AAxpq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "278.0"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_steps_per_epoch = np.ceil(2.0*neg/BATCH_SIZE)\n",
    "resampled_steps_per_epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XZ1BvEpcBVHP"
   },
   "source": [
    "### Train on the oversampled data\n",
    "\n",
    "Now try training the model with the resampled data set instead of using class weights to see how these methods compare.\n",
    "\n",
    "Note: Because the data was balanced by replicating the positive examples, the total dataset size is larger, and each epoch runs for more training steps. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "soRQ89JYqd6b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "278/278 [==============================] - 44s 150ms/step - loss: 0.5857 - tp: 190312.0000 - fp: 28125.0000 - tn: 313432.0000 - fn: 94437.0000 - accuracy: 0.8043 - precision: 0.8712 - recall: 0.6684 - auc: 0.8491 - prc: 0.8716 - val_loss: 0.1342 - val_tp: 68.0000 - val_fp: 336.0000 - val_tn: 45154.0000 - val_fn: 11.0000 - val_accuracy: 0.9924 - val_precision: 0.1683 - val_recall: 0.8608 - val_auc: 0.9426 - val_prc: 0.7307\n",
      "Epoch 2/100\n",
      "278/278 [==============================] - 39s 139ms/step - loss: 0.2078 - tp: 253412.0000 - fp: 16169.0000 - tn: 268365.0000 - fn: 31398.0000 - accuracy: 0.9165 - precision: 0.9400 - recall: 0.8898 - auc: 0.9761 - prc: 0.9777 - val_loss: 0.0714 - val_tp: 68.0000 - val_fp: 620.0000 - val_tn: 44870.0000 - val_fn: 11.0000 - val_accuracy: 0.9862 - val_precision: 0.0988 - val_recall: 0.8608 - val_auc: 0.9541 - val_prc: 0.7194\n",
      "Epoch 3/100\n",
      "278/278 [==============================] - 37s 133ms/step - loss: 0.1568 - tp: 265593.0000 - fp: 13864.0000 - tn: 270688.0000 - fn: 19199.0000 - accuracy: 0.9419 - precision: 0.9504 - recall: 0.9326 - auc: 0.9870 - prc: 0.9872 - val_loss: 0.0578 - val_tp: 69.0000 - val_fp: 693.0000 - val_tn: 44797.0000 - val_fn: 10.0000 - val_accuracy: 0.9846 - val_precision: 0.0906 - val_recall: 0.8734 - val_auc: 0.9578 - val_prc: 0.7253\n",
      "Epoch 4/100\n",
      "278/278 [==============================] - 40s 146ms/step - loss: 0.1365 - tp: 271373.0000 - fp: 13730.0000 - tn: 271148.0000 - fn: 13093.0000 - accuracy: 0.9529 - precision: 0.9518 - recall: 0.9540 - auc: 0.9902 - prc: 0.9900 - val_loss: 0.0528 - val_tp: 69.0000 - val_fp: 751.0000 - val_tn: 44739.0000 - val_fn: 10.0000 - val_accuracy: 0.9833 - val_precision: 0.0841 - val_recall: 0.8734 - val_auc: 0.9611 - val_prc: 0.7304\n",
      "Epoch 5/100\n",
      "278/278 [==============================] - 38s 136ms/step - loss: 0.1236 - tp: 274585.0000 - fp: 14262.0000 - tn: 270487.0000 - fn: 10010.0000 - accuracy: 0.9574 - precision: 0.9506 - recall: 0.9648 - auc: 0.9919 - prc: 0.9916 - val_loss: 0.0494 - val_tp: 69.0000 - val_fp: 742.0000 - val_tn: 44748.0000 - val_fn: 10.0000 - val_accuracy: 0.9835 - val_precision: 0.0851 - val_recall: 0.8734 - val_auc: 0.9580 - val_prc: 0.7304\n",
      "Epoch 6/100\n",
      "278/278 [==============================] - 39s 140ms/step - loss: 0.1130 - tp: 277270.0000 - fp: 14229.0000 - tn: 270436.0000 - fn: 7409.0000 - accuracy: 0.9620 - precision: 0.9512 - recall: 0.9740 - auc: 0.9932 - prc: 0.9928 - val_loss: 0.0466 - val_tp: 69.0000 - val_fp: 735.0000 - val_tn: 44755.0000 - val_fn: 10.0000 - val_accuracy: 0.9837 - val_precision: 0.0858 - val_recall: 0.8734 - val_auc: 0.9544 - val_prc: 0.7323\n",
      "Epoch 7/100\n",
      "278/278 [==============================] - 37s 133ms/step - loss: 0.1045 - tp: 279380.0000 - fp: 14233.0000 - tn: 270379.0000 - fn: 5352.0000 - accuracy: 0.9656 - precision: 0.9515 - recall: 0.9812 - auc: 0.9941 - prc: 0.9937 - val_loss: 0.0437 - val_tp: 70.0000 - val_fp: 719.0000 - val_tn: 44771.0000 - val_fn: 9.0000 - val_accuracy: 0.9840 - val_precision: 0.0887 - val_recall: 0.8861 - val_auc: 0.9506 - val_prc: 0.7328\n",
      "Epoch 8/100\n",
      "278/278 [==============================] - 38s 137ms/step - loss: 0.0970 - tp: 280229.0000 - fp: 13963.0000 - tn: 270518.0000 - fn: 4634.0000 - accuracy: 0.9673 - precision: 0.9525 - recall: 0.9837 - auc: 0.9948 - prc: 0.9944 - val_loss: 0.0415 - val_tp: 70.0000 - val_fp: 695.0000 - val_tn: 44795.0000 - val_fn: 9.0000 - val_accuracy: 0.9846 - val_precision: 0.0915 - val_recall: 0.8861 - val_auc: 0.9514 - val_prc: 0.7330\n",
      "Epoch 9/100\n",
      "278/278 [==============================] - 39s 139ms/step - loss: 0.0905 - tp: 280893.0000 - fp: 13467.0000 - tn: 271080.0000 - fn: 3904.0000 - accuracy: 0.9695 - precision: 0.9542 - recall: 0.9863 - auc: 0.9954 - prc: 0.9950 - val_loss: 0.0397 - val_tp: 70.0000 - val_fp: 675.0000 - val_tn: 44815.0000 - val_fn: 9.0000 - val_accuracy: 0.9850 - val_precision: 0.0940 - val_recall: 0.8861 - val_auc: 0.9466 - val_prc: 0.7335\n",
      "Epoch 10/100\n",
      "278/278 [==============================] - 38s 136ms/step - loss: 0.0843 - tp: 281344.0000 - fp: 13044.0000 - tn: 271825.0000 - fn: 3131.0000 - accuracy: 0.9716 - precision: 0.9557 - recall: 0.9890 - auc: 0.9959 - prc: 0.9955 - val_loss: 0.0376 - val_tp: 70.0000 - val_fp: 642.0000 - val_tn: 44848.0000 - val_fn: 9.0000 - val_accuracy: 0.9857 - val_precision: 0.0983 - val_recall: 0.8861 - val_auc: 0.9471 - val_prc: 0.7337\n",
      "Epoch 11/100\n",
      "278/278 [==============================] - 37s 134ms/step - loss: 0.0791 - tp: 282388.0000 - fp: 12602.0000 - tn: 271832.0000 - fn: 2522.0000 - accuracy: 0.9734 - precision: 0.9573 - recall: 0.9911 - auc: 0.9963 - prc: 0.9959 - val_loss: 0.0361 - val_tp: 70.0000 - val_fp: 617.0000 - val_tn: 44873.0000 - val_fn: 9.0000 - val_accuracy: 0.9863 - val_precision: 0.1019 - val_recall: 0.8861 - val_auc: 0.9419 - val_prc: 0.7330\n",
      "Epoch 12/100\n",
      "278/278 [==============================] - 37s 134ms/step - loss: 0.0751 - tp: 282524.0000 - fp: 12242.0000 - tn: 272462.0000 - fn: 2116.0000 - accuracy: 0.9748 - precision: 0.9585 - recall: 0.9926 - auc: 0.9965 - prc: 0.9961 - val_loss: 0.0344 - val_tp: 69.0000 - val_fp: 595.0000 - val_tn: 44895.0000 - val_fn: 10.0000 - val_accuracy: 0.9867 - val_precision: 0.1039 - val_recall: 0.8734 - val_auc: 0.9370 - val_prc: 0.7326\n",
      "Epoch 13/100\n",
      "278/278 [==============================] - 35s 125ms/step - loss: 0.0706 - tp: 282660.0000 - fp: 11609.0000 - tn: 273229.0000 - fn: 1846.0000 - accuracy: 0.9764 - precision: 0.9605 - recall: 0.9935 - auc: 0.9968 - prc: 0.9964 - val_loss: 0.0333 - val_tp: 69.0000 - val_fp: 556.0000 - val_tn: 44934.0000 - val_fn: 10.0000 - val_accuracy: 0.9876 - val_precision: 0.1104 - val_recall: 0.8734 - val_auc: 0.9376 - val_prc: 0.7327\n",
      "Epoch 14/100\n",
      "278/278 [==============================] - 36s 131ms/step - loss: 0.0673 - tp: 282535.0000 - fp: 11387.0000 - tn: 273793.0000 - fn: 1629.0000 - accuracy: 0.9771 - precision: 0.9613 - recall: 0.9943 - auc: 0.9970 - prc: 0.9966 - val_loss: 0.0320 - val_tp: 69.0000 - val_fp: 526.0000 - val_tn: 44964.0000 - val_fn: 10.0000 - val_accuracy: 0.9882 - val_precision: 0.1160 - val_recall: 0.8734 - val_auc: 0.9381 - val_prc: 0.7331\n",
      "Epoch 15/100\n",
      "278/278 [==============================] - 37s 133ms/step - loss: 0.0641 - tp: 283509.0000 - fp: 10878.0000 - tn: 273646.0000 - fn: 1311.0000 - accuracy: 0.9786 - precision: 0.9630 - recall: 0.9954 - auc: 0.9971 - prc: 0.9968 - val_loss: 0.0311 - val_tp: 69.0000 - val_fp: 504.0000 - val_tn: 44986.0000 - val_fn: 10.0000 - val_accuracy: 0.9887 - val_precision: 0.1204 - val_recall: 0.8734 - val_auc: 0.9386 - val_prc: 0.7335\n",
      "Epoch 16/100\n",
      "278/278 [==============================] - 37s 132ms/step - loss: 0.0622 - tp: 283906.0000 - fp: 10697.0000 - tn: 273721.0000 - fn: 1020.0000 - accuracy: 0.9794 - precision: 0.9637 - recall: 0.9964 - auc: 0.9971 - prc: 0.9968 - val_loss: 0.0302 - val_tp: 69.0000 - val_fp: 485.0000 - val_tn: 45005.0000 - val_fn: 10.0000 - val_accuracy: 0.9891 - val_precision: 0.1245 - val_recall: 0.8734 - val_auc: 0.9389 - val_prc: 0.7331\n",
      "Epoch 17/100\n",
      "278/278 [==============================] - 35s 124ms/step - loss: 0.0590 - tp: 283207.0000 - fp: 10055.0000 - tn: 275309.0000 - fn: 773.0000 - accuracy: 0.9810 - precision: 0.9657 - recall: 0.9973 - auc: 0.9973 - prc: 0.9969 - val_loss: 0.0299 - val_tp: 69.0000 - val_fp: 477.0000 - val_tn: 45013.0000 - val_fn: 10.0000 - val_accuracy: 0.9893 - val_precision: 0.1264 - val_recall: 0.8734 - val_auc: 0.9393 - val_prc: 0.7331\n",
      "Epoch 18/100\n",
      "278/278 [==============================] - 38s 137ms/step - loss: 0.0568 - tp: 283894.0000 - fp: 9844.0000 - tn: 274917.0000 - fn: 689.0000 - accuracy: 0.9815 - precision: 0.9665 - recall: 0.9976 - auc: 0.9974 - prc: 0.9971 - val_loss: 0.0298 - val_tp: 69.0000 - val_fp: 463.0000 - val_tn: 45027.0000 - val_fn: 10.0000 - val_accuracy: 0.9896 - val_precision: 0.1297 - val_recall: 0.8734 - val_auc: 0.9396 - val_prc: 0.7343\n",
      "Epoch 19/100\n",
      "278/278 [==============================] - 36s 130ms/step - loss: 0.0549 - tp: 283710.0000 - fp: 9500.0000 - tn: 275435.0000 - fn: 699.0000 - accuracy: 0.9821 - precision: 0.9676 - recall: 0.9975 - auc: 0.9974 - prc: 0.9971 - val_loss: 0.0290 - val_tp: 69.0000 - val_fp: 439.0000 - val_tn: 45051.0000 - val_fn: 10.0000 - val_accuracy: 0.9901 - val_precision: 0.1358 - val_recall: 0.8734 - val_auc: 0.9398 - val_prc: 0.7347\n",
      "Epoch 20/100\n",
      "278/278 [==============================] - 38s 136ms/step - loss: 0.0533 - tp: 284301.0000 - fp: 9213.0000 - tn: 275201.0000 - fn: 629.0000 - accuracy: 0.9827 - precision: 0.9686 - recall: 0.9978 - auc: 0.9975 - prc: 0.9972 - val_loss: 0.0288 - val_tp: 69.0000 - val_fp: 428.0000 - val_tn: 45062.0000 - val_fn: 10.0000 - val_accuracy: 0.9904 - val_precision: 0.1388 - val_recall: 0.8734 - val_auc: 0.9401 - val_prc: 0.7355\n",
      "Epoch 21/100\n",
      "278/278 [==============================] - 37s 134ms/step - loss: 0.0514 - tp: 283717.0000 - fp: 8903.0000 - tn: 276105.0000 - fn: 619.0000 - accuracy: 0.9833 - precision: 0.9696 - recall: 0.9978 - auc: 0.9976 - prc: 0.9973 - val_loss: 0.0287 - val_tp: 69.0000 - val_fp: 420.0000 - val_tn: 45070.0000 - val_fn: 10.0000 - val_accuracy: 0.9906 - val_precision: 0.1411 - val_recall: 0.8734 - val_auc: 0.9403 - val_prc: 0.7350\n",
      "Epoch 22/100\n",
      "278/278 [==============================] - 33s 120ms/step - loss: 0.0492 - tp: 284646.0000 - fp: 8514.0000 - tn: 275600.0000 - fn: 584.0000 - accuracy: 0.9840 - precision: 0.9710 - recall: 0.9980 - auc: 0.9977 - prc: 0.9974 - val_loss: 0.0281 - val_tp: 69.0000 - val_fp: 393.0000 - val_tn: 45097.0000 - val_fn: 10.0000 - val_accuracy: 0.9912 - val_precision: 0.1494 - val_recall: 0.8734 - val_auc: 0.9405 - val_prc: 0.7355\n",
      "Epoch 23/100\n",
      "278/278 [==============================] - 32s 117ms/step - loss: 0.0480 - tp: 284626.0000 - fp: 8264.0000 - tn: 275911.0000 - fn: 543.0000 - accuracy: 0.9845 - precision: 0.9718 - recall: 0.9981 - auc: 0.9978 - prc: 0.9975 - val_loss: 0.0278 - val_tp: 69.0000 - val_fp: 369.0000 - val_tn: 45121.0000 - val_fn: 10.0000 - val_accuracy: 0.9917 - val_precision: 0.1575 - val_recall: 0.8734 - val_auc: 0.9406 - val_prc: 0.7363\n",
      "Epoch 24/100\n",
      "278/278 [==============================] - 31s 113ms/step - loss: 0.0461 - tp: 284790.0000 - fp: 7947.0000 - tn: 276182.0000 - fn: 425.0000 - accuracy: 0.9853 - precision: 0.9729 - recall: 0.9985 - auc: 0.9979 - prc: 0.9975 - val_loss: 0.0280 - val_tp: 69.0000 - val_fp: 359.0000 - val_tn: 45131.0000 - val_fn: 10.0000 - val_accuracy: 0.9919 - val_precision: 0.1612 - val_recall: 0.8734 - val_auc: 0.9408 - val_prc: 0.7368\n",
      "Epoch 25/100\n",
      "278/278 [==============================] - 33s 119ms/step - loss: 0.0453 - tp: 283645.0000 - fp: 7717.0000 - tn: 277553.0000 - fn: 429.0000 - accuracy: 0.9857 - precision: 0.9735 - recall: 0.9985 - auc: 0.9979 - prc: 0.9975 - val_loss: 0.0273 - val_tp: 69.0000 - val_fp: 331.0000 - val_tn: 45159.0000 - val_fn: 10.0000 - val_accuracy: 0.9925 - val_precision: 0.1725 - val_recall: 0.8734 - val_auc: 0.9409 - val_prc: 0.7361\n",
      "Epoch 26/100\n",
      "278/278 [==============================] - 33s 118ms/step - loss: 0.0437 - tp: 284562.0000 - fp: 7476.0000 - tn: 276939.0000 - fn: 367.0000 - accuracy: 0.9862 - precision: 0.9744 - recall: 0.9987 - auc: 0.9979 - prc: 0.9976 - val_loss: 0.0273 - val_tp: 69.0000 - val_fp: 323.0000 - val_tn: 45167.0000 - val_fn: 10.0000 - val_accuracy: 0.9927 - val_precision: 0.1760 - val_recall: 0.8734 - val_auc: 0.9410 - val_prc: 0.7350\n",
      "Epoch 27/100\n",
      "278/278 [==============================] - 32s 115ms/step - loss: 0.0430 - tp: 284727.0000 - fp: 7360.0000 - tn: 276875.0000 - fn: 382.0000 - accuracy: 0.9864 - precision: 0.9748 - recall: 0.9987 - auc: 0.9979 - prc: 0.9976 - val_loss: 0.0276 - val_tp: 69.0000 - val_fp: 311.0000 - val_tn: 45179.0000 - val_fn: 10.0000 - val_accuracy: 0.9930 - val_precision: 0.1816 - val_recall: 0.8734 - val_auc: 0.9411 - val_prc: 0.7340\n",
      "Epoch 28/100\n",
      "278/278 [==============================] - 33s 120ms/step - loss: 0.0416 - tp: 284456.0000 - fp: 7131.0000 - tn: 277467.0000 - fn: 290.0000 - accuracy: 0.9870 - precision: 0.9755 - recall: 0.9990 - auc: 0.9980 - prc: 0.9977 - val_loss: 0.0274 - val_tp: 69.0000 - val_fp: 296.0000 - val_tn: 45194.0000 - val_fn: 10.0000 - val_accuracy: 0.9933 - val_precision: 0.1890 - val_recall: 0.8734 - val_auc: 0.9412 - val_prc: 0.7332\n",
      "Epoch 29/100\n",
      "278/278 [==============================] - 31s 112ms/step - loss: 0.0409 - tp: 284417.0000 - fp: 6996.0000 - tn: 277693.0000 - fn: 238.0000 - accuracy: 0.9873 - precision: 0.9760 - recall: 0.9992 - auc: 0.9980 - prc: 0.9977 - val_loss: 0.0274 - val_tp: 69.0000 - val_fp: 283.0000 - val_tn: 45207.0000 - val_fn: 10.0000 - val_accuracy: 0.9936 - val_precision: 0.1960 - val_recall: 0.8734 - val_auc: 0.9412 - val_prc: 0.7327\n",
      "Epoch 30/100\n",
      "278/278 [==============================] - 32s 116ms/step - loss: 0.0392 - tp: 283961.0000 - fp: 6656.0000 - tn: 278557.0000 - fn: 170.0000 - accuracy: 0.9880 - precision: 0.9771 - recall: 0.9994 - auc: 0.9981 - prc: 0.9978 - val_loss: 0.0277 - val_tp: 69.0000 - val_fp: 279.0000 - val_tn: 45211.0000 - val_fn: 10.0000 - val_accuracy: 0.9937 - val_precision: 0.1983 - val_recall: 0.8734 - val_auc: 0.9413 - val_prc: 0.7317\n",
      "Epoch 31/100\n",
      "278/278 [==============================] - 32s 116ms/step - loss: 0.0384 - tp: 283867.0000 - fp: 6472.0000 - tn: 278880.0000 - fn: 125.0000 - accuracy: 0.9884 - precision: 0.9777 - recall: 0.9996 - auc: 0.9981 - prc: 0.9978 - val_loss: 0.0275 - val_tp: 69.0000 - val_fp: 269.0000 - val_tn: 45221.0000 - val_fn: 10.0000 - val_accuracy: 0.9939 - val_precision: 0.2041 - val_recall: 0.8734 - val_auc: 0.9352 - val_prc: 0.7295\n",
      "Epoch 32/100\n",
      "278/278 [==============================] - 31s 112ms/step - loss: 0.0376 - tp: 284966.0000 - fp: 6325.0000 - tn: 278028.0000 - fn: 25.0000 - accuracy: 0.9888 - precision: 0.9783 - recall: 0.9999 - auc: 0.9981 - prc: 0.9978 - val_loss: 0.0279 - val_tp: 68.0000 - val_fp: 264.0000 - val_tn: 45226.0000 - val_fn: 11.0000 - val_accuracy: 0.9940 - val_precision: 0.2048 - val_recall: 0.8608 - val_auc: 0.9352 - val_prc: 0.7295\n",
      "Epoch 33/100\n",
      "278/278 [==============================] - 33s 121ms/step - loss: 0.0366 - tp: 284013.0000 - fp: 6114.0000 - tn: 279215.0000 - fn: 2.0000 - accuracy: 0.9893 - precision: 0.9789 - recall: 1.0000 - auc: 0.9982 - prc: 0.9979 - val_loss: 0.0279 - val_tp: 68.0000 - val_fp: 252.0000 - val_tn: 45238.0000 - val_fn: 11.0000 - val_accuracy: 0.9942 - val_precision: 0.2125 - val_recall: 0.8608 - val_auc: 0.9353 - val_prc: 0.7278\n",
      "Epoch 34/100\n",
      "278/278 [==============================] - 33s 118ms/step - loss: 0.0357 - tp: 284772.0000 - fp: 5930.0000 - tn: 278642.0000 - fn: 0.0000e+00 - accuracy: 0.9896 - precision: 0.9796 - recall: 1.0000 - auc: 0.9982 - prc: 0.9979 - val_loss: 0.0280 - val_tp: 68.0000 - val_fp: 244.0000 - val_tn: 45246.0000 - val_fn: 11.0000 - val_accuracy: 0.9944 - val_precision: 0.2179 - val_recall: 0.8608 - val_auc: 0.9353 - val_prc: 0.7273\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00034: early stopping\n"
     ]
    }
   ],
   "source": [
    "resampled_model = make_model()\n",
    "resampled_model.load_weights(initial_weights)\n",
    "\n",
    "# Reset the bias to zero, since this dataset is balanced.\n",
    "output_layer = resampled_model.layers[-1] \n",
    "output_layer.bias.assign([0])\n",
    "\n",
    "val_ds = tf.data.Dataset.from_tensor_slices((val_features, val_labels)).cache()\n",
    "val_ds = val_ds.batch(BATCH_SIZE).prefetch(2) \n",
    "\n",
    "resampled_history = resampled_model.fit(\n",
    "    resampled_ds,\n",
    "    epochs=EPOCHS,\n",
    "    steps_per_epoch=resampled_steps_per_epoch,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=val_ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "avALvzUp3T_c"
   },
   "source": [
    "If the training process were considering the whole dataset on each gradient update, this oversampling would be basically identical to the class weighting.\n",
    "\n",
    "But when training the model batch-wise, as you did here, the oversampled data provides a smoother gradient signal: Instead of each positive example being shown in one batch with a large weight, they're shown in many different batches each time with a small weight. \n",
    "\n",
    "This smoother gradient signal makes it easier to train the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "klHZ0HV76VC5"
   },
   "source": [
    "### Check training history\n",
    "\n",
    "Note that the distributions of metrics will be different here, because the training data has a totally different distribution from the validation and test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "YoUGfr1vuivl"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(resampled_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1PuH3A2vnwrh"
   },
   "source": [
    "### Re-train\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KFLxRL8eoDE5"
   },
   "source": [
    "Because training is easier on the balanced data, the above training procedure may overfit quickly. \n",
    "\n",
    "So break up the epochs to give the `tf.keras.callbacks.EarlyStopping` finer control over when to stop training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "e_yn9I26qAHU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "20/20 [==============================] - 6s 190ms/step - loss: 1.9363 - tp: 4567.0000 - fp: 2280.0000 - tn: 63647.0000 - fn: 16035.0000 - accuracy: 0.7883 - precision: 0.6670 - recall: 0.2217 - auc: 0.7228 - prc: 0.5019 - val_loss: 0.2956 - val_tp: 13.0000 - val_fp: 393.0000 - val_tn: 45097.0000 - val_fn: 66.0000 - val_accuracy: 0.9899 - val_precision: 0.0320 - val_recall: 0.1646 - val_auc: 0.4617 - val_prc: 0.0892\n",
      "Epoch 2/1000\n",
      "20/20 [==============================] - 3s 145ms/step - loss: 1.1243 - tp: 8322.0000 - fp: 2560.0000 - tn: 17992.0000 - fn: 12086.0000 - accuracy: 0.6424 - precision: 0.7647 - recall: 0.4078 - auc: 0.5815 - prc: 0.7016 - val_loss: 0.3046 - val_tp: 49.0000 - val_fp: 590.0000 - val_tn: 44900.0000 - val_fn: 30.0000 - val_accuracy: 0.9864 - val_precision: 0.0767 - val_recall: 0.6203 - val_auc: 0.8330 - val_prc: 0.4933\n",
      "Epoch 3/1000\n",
      "20/20 [==============================] - 3s 146ms/step - loss: 0.7736 - tp: 11254.0000 - fp: 2672.0000 - tn: 17837.0000 - fn: 9197.0000 - accuracy: 0.7102 - precision: 0.8081 - recall: 0.5503 - auc: 0.7071 - prc: 0.7951 - val_loss: 0.2991 - val_tp: 63.0000 - val_fp: 576.0000 - val_tn: 44914.0000 - val_fn: 16.0000 - val_accuracy: 0.9870 - val_precision: 0.0986 - val_recall: 0.7975 - val_auc: 0.9008 - val_prc: 0.6476\n",
      "Epoch 4/1000\n",
      "20/20 [==============================] - 3s 140ms/step - loss: 0.6216 - tp: 12882.0000 - fp: 2751.0000 - tn: 17680.0000 - fn: 7647.0000 - accuracy: 0.7461 - precision: 0.8240 - recall: 0.6275 - auc: 0.7666 - prc: 0.8380 - val_loss: 0.2846 - val_tp: 64.0000 - val_fp: 456.0000 - val_tn: 45034.0000 - val_fn: 15.0000 - val_accuracy: 0.9897 - val_precision: 0.1231 - val_recall: 0.8101 - val_auc: 0.9146 - val_prc: 0.6579\n",
      "Epoch 5/1000\n",
      "20/20 [==============================] - 2s 128ms/step - loss: 0.5276 - tp: 13716.0000 - fp: 2541.0000 - tn: 17918.0000 - fn: 6785.0000 - accuracy: 0.7723 - precision: 0.8437 - recall: 0.6690 - auc: 0.8117 - prc: 0.8654 - val_loss: 0.2659 - val_tp: 67.0000 - val_fp: 342.0000 - val_tn: 45148.0000 - val_fn: 12.0000 - val_accuracy: 0.9922 - val_precision: 0.1638 - val_recall: 0.8481 - val_auc: 0.9189 - val_prc: 0.6747\n",
      "Epoch 6/1000\n",
      "20/20 [==============================] - 3s 136ms/step - loss: 0.4728 - tp: 14163.0000 - fp: 2326.0000 - tn: 18113.0000 - fn: 6358.0000 - accuracy: 0.7880 - precision: 0.8589 - recall: 0.6902 - auc: 0.8401 - prc: 0.8822 - val_loss: 0.2460 - val_tp: 67.0000 - val_fp: 275.0000 - val_tn: 45215.0000 - val_fn: 12.0000 - val_accuracy: 0.9937 - val_precision: 0.1959 - val_recall: 0.8481 - val_auc: 0.9219 - val_prc: 0.7110\n",
      "Epoch 7/1000\n",
      "20/20 [==============================] - 2s 121ms/step - loss: 0.4322 - tp: 14591.0000 - fp: 2123.0000 - tn: 18307.0000 - fn: 5939.0000 - accuracy: 0.8032 - precision: 0.8730 - recall: 0.7107 - auc: 0.8628 - prc: 0.8971 - val_loss: 0.2265 - val_tp: 67.0000 - val_fp: 230.0000 - val_tn: 45260.0000 - val_fn: 12.0000 - val_accuracy: 0.9947 - val_precision: 0.2256 - val_recall: 0.8481 - val_auc: 0.9249 - val_prc: 0.7092\n",
      "Epoch 8/1000\n",
      "20/20 [==============================] - 3s 130ms/step - loss: 0.3997 - tp: 14779.0000 - fp: 2008.0000 - tn: 18583.0000 - fn: 5590.0000 - accuracy: 0.8145 - precision: 0.8804 - recall: 0.7256 - auc: 0.8826 - prc: 0.9079 - val_loss: 0.2084 - val_tp: 67.0000 - val_fp: 238.0000 - val_tn: 45252.0000 - val_fn: 12.0000 - val_accuracy: 0.9945 - val_precision: 0.2197 - val_recall: 0.8481 - val_auc: 0.9278 - val_prc: 0.7178\n",
      "Epoch 9/1000\n",
      "20/20 [==============================] - 3s 159ms/step - loss: 0.3734 - tp: 15290.0000 - fp: 1823.0000 - tn: 18658.0000 - fn: 5189.0000 - accuracy: 0.8288 - precision: 0.8935 - recall: 0.7466 - auc: 0.9008 - prc: 0.9202 - val_loss: 0.1926 - val_tp: 68.0000 - val_fp: 235.0000 - val_tn: 45255.0000 - val_fn: 11.0000 - val_accuracy: 0.9946 - val_precision: 0.2244 - val_recall: 0.8608 - val_auc: 0.9309 - val_prc: 0.7152\n",
      "Epoch 10/1000\n",
      "20/20 [==============================] - 3s 145ms/step - loss: 0.3460 - tp: 15640.0000 - fp: 1716.0000 - tn: 18758.0000 - fn: 4846.0000 - accuracy: 0.8398 - precision: 0.9011 - recall: 0.7634 - auc: 0.9163 - prc: 0.9306 - val_loss: 0.1779 - val_tp: 68.0000 - val_fp: 251.0000 - val_tn: 45239.0000 - val_fn: 11.0000 - val_accuracy: 0.9943 - val_precision: 0.2132 - val_recall: 0.8608 - val_auc: 0.9334 - val_prc: 0.7190\n",
      "Epoch 11/1000\n",
      "20/20 [==============================] - 3s 140ms/step - loss: 0.3284 - tp: 16005.0000 - fp: 1577.0000 - tn: 18869.0000 - fn: 4509.0000 - accuracy: 0.8514 - precision: 0.9103 - recall: 0.7802 - auc: 0.9277 - prc: 0.9390 - val_loss: 0.1649 - val_tp: 68.0000 - val_fp: 264.0000 - val_tn: 45226.0000 - val_fn: 11.0000 - val_accuracy: 0.9940 - val_precision: 0.2048 - val_recall: 0.8608 - val_auc: 0.9357 - val_prc: 0.7260\n",
      "Epoch 12/1000\n",
      "20/20 [==============================] - 2s 125ms/step - loss: 0.3079 - tp: 16327.0000 - fp: 1451.0000 - tn: 19024.0000 - fn: 4158.0000 - accuracy: 0.8631 - precision: 0.9184 - recall: 0.7970 - auc: 0.9399 - prc: 0.9479 - val_loss: 0.1533 - val_tp: 68.0000 - val_fp: 282.0000 - val_tn: 45208.0000 - val_fn: 11.0000 - val_accuracy: 0.9936 - val_precision: 0.1943 - val_recall: 0.8608 - val_auc: 0.9375 - val_prc: 0.7269\n",
      "Epoch 13/1000\n",
      "20/20 [==============================] - 2s 111ms/step - loss: 0.2988 - tp: 16683.0000 - fp: 1466.0000 - tn: 18910.0000 - fn: 3901.0000 - accuracy: 0.8690 - precision: 0.9192 - recall: 0.8105 - auc: 0.9449 - prc: 0.9518 - val_loss: 0.1426 - val_tp: 68.0000 - val_fp: 290.0000 - val_tn: 45200.0000 - val_fn: 11.0000 - val_accuracy: 0.9934 - val_precision: 0.1899 - val_recall: 0.8608 - val_auc: 0.9395 - val_prc: 0.7291\n",
      "Epoch 14/1000\n",
      "20/20 [==============================] - 3s 143ms/step - loss: 0.2776 - tp: 17096.0000 - fp: 1378.0000 - tn: 19128.0000 - fn: 3358.0000 - accuracy: 0.8844 - precision: 0.9254 - recall: 0.8358 - auc: 0.9541 - prc: 0.9595 - val_loss: 0.1340 - val_tp: 68.0000 - val_fp: 331.0000 - val_tn: 45159.0000 - val_fn: 11.0000 - val_accuracy: 0.9925 - val_precision: 0.1704 - val_recall: 0.8608 - val_auc: 0.9415 - val_prc: 0.7308\n",
      "Epoch 15/1000\n",
      "20/20 [==============================] - 3s 140ms/step - loss: 0.2613 - tp: 17334.0000 - fp: 1301.0000 - tn: 19214.0000 - fn: 3111.0000 - accuracy: 0.8923 - precision: 0.9302 - recall: 0.8478 - auc: 0.9600 - prc: 0.9645 - val_loss: 0.1267 - val_tp: 68.0000 - val_fp: 383.0000 - val_tn: 45107.0000 - val_fn: 11.0000 - val_accuracy: 0.9914 - val_precision: 0.1508 - val_recall: 0.8608 - val_auc: 0.9440 - val_prc: 0.7233\n",
      "Epoch 16/1000\n",
      "20/20 [==============================] - 3s 158ms/step - loss: 0.2524 - tp: 17583.0000 - fp: 1325.0000 - tn: 19110.0000 - fn: 2942.0000 - accuracy: 0.8958 - precision: 0.9299 - recall: 0.8567 - auc: 0.9628 - prc: 0.9669 - val_loss: 0.1199 - val_tp: 68.0000 - val_fp: 435.0000 - val_tn: 45055.0000 - val_fn: 11.0000 - val_accuracy: 0.9902 - val_precision: 0.1352 - val_recall: 0.8608 - val_auc: 0.9455 - val_prc: 0.7142\n",
      "Epoch 17/1000\n",
      "20/20 [==============================] - 3s 137ms/step - loss: 0.2392 - tp: 17746.0000 - fp: 1303.0000 - tn: 19128.0000 - fn: 2783.0000 - accuracy: 0.9002 - precision: 0.9316 - recall: 0.8644 - auc: 0.9669 - prc: 0.9702 - val_loss: 0.1130 - val_tp: 68.0000 - val_fp: 464.0000 - val_tn: 45026.0000 - val_fn: 11.0000 - val_accuracy: 0.9896 - val_precision: 0.1278 - val_recall: 0.8608 - val_auc: 0.9472 - val_prc: 0.7173\n",
      "Epoch 18/1000\n",
      "20/20 [==============================] - 3s 146ms/step - loss: 0.2261 - tp: 17921.0000 - fp: 1242.0000 - tn: 19175.0000 - fn: 2622.0000 - accuracy: 0.9057 - precision: 0.9352 - recall: 0.8724 - auc: 0.9707 - prc: 0.9737 - val_loss: 0.1070 - val_tp: 68.0000 - val_fp: 495.0000 - val_tn: 44995.0000 - val_fn: 11.0000 - val_accuracy: 0.9889 - val_precision: 0.1208 - val_recall: 0.8608 - val_auc: 0.9487 - val_prc: 0.7191\n",
      "Epoch 19/1000\n",
      "20/20 [==============================] - 2s 124ms/step - loss: 0.2204 - tp: 17914.0000 - fp: 1250.0000 - tn: 19314.0000 - fn: 2482.0000 - accuracy: 0.9089 - precision: 0.9348 - recall: 0.8783 - auc: 0.9720 - prc: 0.9742 - val_loss: 0.1016 - val_tp: 68.0000 - val_fp: 514.0000 - val_tn: 44976.0000 - val_fn: 11.0000 - val_accuracy: 0.9885 - val_precision: 0.1168 - val_recall: 0.8608 - val_auc: 0.9497 - val_prc: 0.7230\n",
      "Epoch 20/1000\n",
      "20/20 [==============================] - 3s 145ms/step - loss: 0.2108 - tp: 18072.0000 - fp: 1161.0000 - tn: 19361.0000 - fn: 2366.0000 - accuracy: 0.9139 - precision: 0.9396 - recall: 0.8842 - auc: 0.9753 - prc: 0.9770 - val_loss: 0.0968 - val_tp: 68.0000 - val_fp: 542.0000 - val_tn: 44948.0000 - val_fn: 11.0000 - val_accuracy: 0.9879 - val_precision: 0.1115 - val_recall: 0.8608 - val_auc: 0.9503 - val_prc: 0.7248\n",
      "Epoch 21/1000\n",
      "20/20 [==============================] - 3s 141ms/step - loss: 0.2023 - tp: 18286.0000 - fp: 1080.0000 - tn: 19433.0000 - fn: 2161.0000 - accuracy: 0.9209 - precision: 0.9442 - recall: 0.8943 - auc: 0.9776 - prc: 0.9792 - val_loss: 0.0927 - val_tp: 68.0000 - val_fp: 567.0000 - val_tn: 44923.0000 - val_fn: 11.0000 - val_accuracy: 0.9873 - val_precision: 0.1071 - val_recall: 0.8608 - val_auc: 0.9520 - val_prc: 0.7248\n",
      "Epoch 22/1000\n",
      "20/20 [==============================] - 3s 165ms/step - loss: 0.1998 - tp: 18318.0000 - fp: 1114.0000 - tn: 19389.0000 - fn: 2139.0000 - accuracy: 0.9206 - precision: 0.9427 - recall: 0.8954 - auc: 0.9780 - prc: 0.9793 - val_loss: 0.0889 - val_tp: 68.0000 - val_fp: 581.0000 - val_tn: 44909.0000 - val_fn: 11.0000 - val_accuracy: 0.9870 - val_precision: 0.1048 - val_recall: 0.8608 - val_auc: 0.9516 - val_prc: 0.7246\n",
      "Epoch 23/1000\n",
      "20/20 [==============================] - 3s 135ms/step - loss: 0.1949 - tp: 18459.0000 - fp: 1143.0000 - tn: 19302.0000 - fn: 2056.0000 - accuracy: 0.9219 - precision: 0.9417 - recall: 0.8998 - auc: 0.9791 - prc: 0.9801 - val_loss: 0.0852 - val_tp: 68.0000 - val_fp: 585.0000 - val_tn: 44905.0000 - val_fn: 11.0000 - val_accuracy: 0.9869 - val_precision: 0.1041 - val_recall: 0.8608 - val_auc: 0.9518 - val_prc: 0.7242\n",
      "Epoch 24/1000\n",
      "20/20 [==============================] - 3s 133ms/step - loss: 0.1886 - tp: 18440.0000 - fp: 1076.0000 - tn: 19471.0000 - fn: 1973.0000 - accuracy: 0.9256 - precision: 0.9449 - recall: 0.9033 - auc: 0.9809 - prc: 0.9816 - val_loss: 0.0822 - val_tp: 68.0000 - val_fp: 598.0000 - val_tn: 44892.0000 - val_fn: 11.0000 - val_accuracy: 0.9866 - val_precision: 0.1021 - val_recall: 0.8608 - val_auc: 0.9522 - val_prc: 0.7148\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00024: early stopping\n"
     ]
    }
   ],
   "source": [
    "resampled_model = make_model()\n",
    "resampled_model.load_weights(initial_weights)\n",
    "\n",
    "# Reset the bias to zero, since this dataset is balanced.\n",
    "output_layer = resampled_model.layers[-1] \n",
    "output_layer.bias.assign([0])\n",
    "\n",
    "resampled_history = resampled_model.fit(\n",
    "    resampled_ds,\n",
    "    # These are not real epochs\n",
    "    steps_per_epoch=20,\n",
    "    epochs=10*EPOCHS,\n",
    "    callbacks=[early_stopping],\n",
    "    validation_data=(val_ds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UuJYKv0gpBK1"
   },
   "source": [
    "### Re-check training history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "FMycrpJwn39w"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2jKlTp3LWWWcxY8YMxo8fz6JFi+JdnowiPcEwb+6o5+n1Vby4qZqOnhC5qR5uOmcs18yJdPhw6AJ7kREpZuHaWrvCGFM2wMNvBn4Xq1oGojgrSSPXIgnu+uuvx1rb+/ixxx7r97hXX311aAqSUefdPY08ubaS5zdW09wZICPJzTVzxnDNnDEsLM/GpSkfIiNe3OdcG2OSgSuBu/tstsCLxhgL/Mpa+1Cs6yjOTGJ7TVusv42IiCSgHbVt/ODPW3hlWx0pHieXTS/gQ3PHcMHEPDwuBWqR0STu4Rq4BnjzqCkhi6y1VcaYfOAlY8xWa+2K/p5sjLkDuAOgtLT0tIsozkzilW21WGs1t01ERAZFY0cPP12+nd+u3Eey28m3rp7KJ88tI8njjHdpIhIjwyFc38RRU0KstVXR21pjzFPAAqDfcB0d1X4IYP78+ba/YwaiOCsJfyBMQ0cPuane030ZERERuoMhHn9rLw/8tYLOnhC3LCjlnksnkaOfLyKjXlzDtTEmA1gMfKLPthTAYa1ti96/HPjHWNfS2+u6qUvhWiQOEuVTo75zumX0sdbyl43V/MvzW9nX2MnSKXl86+ppTCpIi3dpIjJEYtmK73fAEiDXGFMJfBdwA1hrH4wedj3worW2o89TC4Cnoj9kXcAT1tq/xKrOQ0qyDrfjmzM2M9bfTkT68Pl8NDQ0kJOTM6oDtrWWhoYGfD5fvEuRGFi/v5l/+vNm3t3TxJSCNB7/9AIumpwX77JEZIjFslvIzQM45jEiLfv6btsFzIlNVcdX3GchGREZWiUlJVRWVlJXVxfvUmLO5/NRUlIS7zJkEB1s6eJHf9nGU+8dIDfVw798eBYfnVeizh8iCWo4zLkeFjKS3KR5XWrHJxIHbreb8vLyeJchckpCYcvP/rqDX762g7CFu5ZO4HOLJ5Dm05LkIolM4bqP4qwkLSQjIiIn5Q+E+OKy93hhUw0fnF3EN66a2ju9UEQSm8J1H8WZSVQ2dca7DBERGcYaO3r4m1+/y3v7m/nuNdO5fZE+dRGRwxSu+yjOSmLVnsaTHygiIglpX0Mntz66iqrmLn758bO5cmZRvEsSkWFG4bqP4swk2vxBWv0B0jVnTkRE+li/v5nP/PpdgmHLE59dyLxx2fEuSUSGIV3K3EdvOz7NuxYRkT5e3lLDTQ+9Q5LHyR/vPF/BWkSOS+G6D7XjExGRo/125V4++/hqJuan8uSdi5iQlxrvkkRkGFO47qN3lUa14xMR6WWMudIYs80Ys8MY841+9mcYY54xxqw3xmwyxtwejzoHm7WW+17Yxt8/tZHFk/NYdse55KVpBV8ROTHNue4jN9WD1+VQuBYRiTLGOIGfA5cBlcC7xpinrbWb+xx2F7DZWnuNMSYP2GaM+a21ticOJQ+KnmCYb/xxA0++d4CbF4zl+9fO1KIwIjIgCtd9GGPUjk9E5EgLgB3R1XMxxiwDrgX6hmsLpJnI2vWpQCMQHOpCB0urP8Cdv1nDmzsa+Mrlk7lr6UQib01E5OQUro9SnJWkOdciIocVA/v7PK4EFh51zM+Ap4EqIA240VobHpryBld1i5/bHl3Fjtp27vvoHG6Yp6XqReTU6DOuoxRnJmlaiIjIYf0N2dqjHl8BrAPGAHOBnxlj0vt9MWPuMMasNsasrqurG8w6z1h7d5CP/PItKpu6ePT2cxSsReS0KFwfpSQrifr2HvyBULxLEREZDiqBsX0elxAZoe7rduBJG7ED2A1M7e/FrLUPWWvnW2vn5+XlxaTg0/XIG7s50BwJ1hdOGl61icjIoXB9lN52fBq9FhEBeBeYZIwpN8Z4gJuITAHpax9wCYAxpgCYAuwa0irPUHNnD/+5YheXTy/gnDL1sBaR06dwfZTiTC0kIyJyiLU2CNwNvABsAX5vrd1kjPmcMeZz0cO+D5xvjHkfeBn4urW2Pj4Vn54HX9tFe0+Qv7t8SrxLEZERThc0HkUj1yIiR7LWPgc8d9S2B/vcrwIuH+q6Bkttm5/H3trNtXPGMKUwLd7liMgIp5HroxSkeXE6jNrxiYgkiF+8spNAyHLPpZPjXYqIjAIK10dxOR0Upvs0LUREJAFUNnXy25V7+dj8EspyU+JdjoiMAgrX/SjOUjs+EZFE8MDLFRhj+H8XT4p3KSIySihc96NEC8mIiIx6O+va+ePaA3xi4TjGZCbFuxwRGSUUrvtRkplEdaufQGhELjAmIiID8O8vbcfrcvD5pRPiXYqIjCIK1/0ozkoibCPL4IqIyOizuaqVZzcc5PZFZeSmeuNdjoiMIjEL18aYR4wxtcaYjcfZv8QY02KMWRf9+oc++640xmwzxuwwxnwjVjUeT2+va827FhEZlX7y0jbSfS7uuFCj1iIyuGI5cv0YcOVJjnndWjs3+vWPAMYYJ/Bz4CpgOnCzMWZ6DOs8xqFe15Wady0iMuqs3dfE8i21/O3iCWQku+NdjoiMMjEL19baFUDjaTx1AbDDWrvLWtsDLAOuHdTiTqIowwdolUYRkdHovhe2kZvq4bbzy+JdioiMQvGec32eMWa9MeZ5Y8yM6LZiYH+fYyqj24aMz+0kL83LgWYtJCMiMpq8taOet3Y28PklE0nxapFiERl88TyzrAXGWWvbjTFXA38CJgGmn2Pt8V7EGHMHcAdAaWnpoBVXol7XIiKjirWWH7+4jaIMH7csHLyfFyIifcUtXFtrW/vcf84Y8wtjTC6RkeqxfQ4tAapO8DoPAQ8BzJ8//7gh/FQVZyax8UDLYL2ciIjE2ctbanlvXzP/8uFZ+NzOeJcjp8FaS9hCIBTGWkjyRP4eq5q7aO4M0NkTpLMnRGdPkGSPi4sm5wHw+Nt7ONDUFd0Xwh8MMTk/jS9eGlk86LZHV7G/sRN/IEx3MIQ/EObiqfk8cPNZAHzqkVWkeV2Mz0uJfOWmMiE/lVR9+iH9iNu/CmNMIVBjrbXGmAVEpqg0AM3AJGNMOXAAuAm4ZajrK85K4sVNNYTDFoejv8F0EREZKcJhy30vbqMsJ5kb5pXEu5yEZ62ltSvYe0HpH1bv59Xtdeyp76ClK0AwZMlO8fDcFy8E4I7HV/PKtloCocNjaBPyUnj575YA8IXfvcfqvU1HfI/ZJRm94fp/3t3Pzrp2kj0uktxOvG4HaX2CcVGGj2SPE5/LidftxOd2MGNMBgChsMXjNGyqauH5jQcJR0v4xLml/NN1swiGwtz7zCbG56YyPi+FSQVpjMnwYczIyg7WWjp6QrR2BQhbS0lWpHPahspm6tq68QfCdAVCdAVCZCS5+dCcMQD854pd1LYd2bq4NCeFT547DoCfv7KDxo6eI/ZPKUjjY+dExlEfeLmCjp4gBoMxkekL08ek88HZkdf/2V8rCIYtDmMwgMNhmDEmnSVT8rHW8l9v7MYYg8OAwxiSPE7mjctiQl5qDP+0Tixm4doY8ztgCZBrjKkEvgu4Aay1DwI3AHcaY4JAF3CTtdYCQWPM3cALgBN4xFq7KVZ1Hk9JZhI9oTD17d3kp/uG+tuLiMgg+vP7B9la3cb9N83F7Yz35UaJIRgK43QYjDGs2dvEiu117GnoYE99B7vrO/AHwmz5/pU4HYYNlS1sPNBCWU4KUwrScDkNmcme3te6dHoBE/NTcTkMLqcDp8OQk3J4/z2XTqa9O0CSx0WKx0mSx0lG0uFOMM/cfcEJB8r+5cOzj7vP6TA8fOs5AHQHQ+xr6GRnXQdjMiPZoK69m2fWH6SlK9D7nIwkN//wwel8ZF4JnT1B9jZ0MjE/dUj+7QVDYfzBcO+o+v7GTvY3dtLY2UNjR+QrHLZ8+fIpAHz7T++zfHMtjZ099AQji+eNz03hr19ZAsAP/ryFlbuP7E8xY0x6b7h+cXM1m6paj9h/Tll2b7j+84aD7Gs8fA2btZYlU/J7w/XvV++nrq0ba8FisRaumTOmN1z//JWddAVCR7z+LQtLo+Ea/unPW475M/jbi8bzzaun4Q+E+M6fNjJjTDozizOYVpQ+JNdamEieHR3mz59vV69ePSiv9detNXz6sdX88c7zmTcua1BeU0TkeIwxa6y18+Ndx1AazHP2iQRDYS7/9xW4nQ6e/+KFCf1pZHNnD1ur29hW3cbW6jY+cW4pM8ZksKmqhZ8uryDV6yLV6yLF6yLV6+RDc4opzUmmts3Ptuo2Ur0ueoJhmjoDNHX2cNXMQjKTPbyyrZb/fnsvTZ09NEUDXKs/yNvfvJiijCT+4+UKfrJ8O2MykijPTaEsN5ny3FQ+vrAUn9uJtXbEjfT2Za2lsaOHnXUdbK9pY/PBVq4/q5hzyrJ5c0c9H394JR6ng0kFqUwvSmf6mHSunlVEQXTwricYpqUrQKs/ELntCnD+hFw8Lgdv7ajn1e11tHZF9rV3B/EHQvz3Zxbiczv56fLt/OadvfgDYfyBEMGwxekw7PznqwH46h/W84c1lUfUW5Th4+1vXgJERp6317SRneIhO8VDepKb7BQPV8woBGBrdSvdgTA+t5MktxOfx0GyxzWkU2IOTQcKW0vYRkax3U4H1lrauoORYB49pqUrgNflYExmEjvr2vnYg2/TEB05NwbKc1P4zgems3RqPv5AiJ5QmHTfqbfkPNE5W5OFjqPvQjIK1yIiI9eTaw+wq76Dhz45b0QHa38ghMfpGNB78AdC7KhtJyPJzdjsZHbUtvPxh9+hprW795jMZDeLJ+cyY0wGnT0h9jd20tETpN0fpL07SCBkOXtcFqU5yby9s4EvLlt3zPeZVpTO3GQPnd0hatv8ZCV7GJuVTFaym6wUD15XZE707ReU89mLxh93rvtIDtYQqT8n1UtOqpcF5dlH7JtamMb9N81l88FWNle18sq2Wv6wppJ547IoSPfxyBu7+cdnNx/zmm9942LGZCaxdl8Tj7+9h4wkN+k+N6m+yNSWUHR+ysT8VC6fUYjP5STJ48DncuJzO3untX7mwnI+Mq+E7BQPWckeMpPdR4ygf/ai8Sd8b1ML0wfhT+jMGGNwGnAe1fPCGHNMMM7u84nGhLxUVn/7Umpau9l4oIVNVa1srGohMzodacX2Olr9wUGfKqaR6+No7w4y87sv8PUrp3LnEq3gJSKxpZHr2OgOhrj4vtfITfPyp8+fP6JC3P+tO8DOug6+fNlkAGbf+wKt/iAelwOfy4HP7eT6s4r55tXTALjlP9/B6TAcaO5iT30HYQt3LZ3AV6+YSnt3kH/4v41MLUxjSmE6UwvTyE/znvDPozsYwmki0zAaO3rYUdtOR3fk+2cmR0Y381K9uDTN5pQd+kXE7XSwbn8zb1TUkZ7kjgToaIieMSb9iJAsg29PfQfJXif5aac+/Vcj16ch1esiM9mtXtciIiPY71bu40BzFz/8yKwRE6y7ekLc+/Qm/mf1fhaUZdOzNIzH5eALl0yizR/EHwzRHZ0CMCE/ctGWjX5c3ukPMSEvlQ/MKmJKYRpzx2YCkZ9pP/nY3FOq49CoM0RGA48ekZXT1zfMzR2b2fv31B8F69gpy02JyesqXJ9AcWaSVmkUERmheoJhfvbKThaWZ3PBxNx4lzMgFTVt3PXEWipq27lr6QS+dOnk3pHhv7nw+B/fG2NYdsd5Q1WmiJyAwvUJFGcmsaehI95liIjIaVhf2Ux9ezf/dN3METFq3dEd5GO/ehuHMfz69gW9beREZGRRuD6B4qwk3txRP+KvYhYRSUSrou3DFg7z6QzdwRBel5MUr4v7PjqHWcUZagErMoLpKoQTKM5MoqMnRHNn4OQHi4jIsPLOrgamFqaR1ad7wHCz5WArV9//Ok+9F2mVdsm0AgVrkRFO4foESrKSgEg7PhERGTmCoTBr9jYN24vwrLU8sXIf1/38TVr9wd5+xyIy8mlayAkcWvqzsqmLmcUZca5GREQGamNVK509oWEZrtv8Ab711EaeWV/FhZNy+cnH5pKX5o13WSIySBSuT6A4UyPXIiIj0ardDQDDMly/s6uR594/yFevmMKdiyeo1ZrIKKNwfQKZyW6SPU614xMRGWFW7mpkfG7KaS0OMdhqWv38ecNB2vxBvnjpJC6bXsDLX14csx67IhJfCtcnYIyJ9LrWQjIiIiNGKGxZtaeRD84uilsNjR09PL/xIM+sr2Ll7kashfnjsvjCJRMxxihYi4xiCtcnUZyVpGkhIiIjyLbqNtr8wSGfEtLmD5DkduJyOvjVip386rVdjM9L4YuXTOKDs8cwMbqaooiMbgrXJ1GcmcS6/c3xLkNERAZoZXS+9cLynJh/r66eEH/dWssz66v467ZaHv7UfC6anMenzivjQ3PGML0oXeskiCQYheuTKM5KorkzQEd3kBSv/rhERIa7VbsbKclKYkz0ovTBYq2lKxAi2eOisaOHz/z6XTZVtdITDJOb6uWWBaW937M4M6n3ongRSSxKiydxqB3fgeYuJhekxbkaERE5EWstq3Y3snjKmS8d3tDezfrKZtbta2ZdZQvr9zdz9axC/uXDs8lMcpPmc3PreeNYOiWfheNzcKrrh4igcH1Sve34mhSuRUSGu5117TR09HDuaU4JWb+/mTljMwH48C/fYm9DJw4DkwvSuGpmIYsn5wPgcBge//SCwSpbREYRheuTOLRKY6UuahQRGfbe2dUInF5/65117Xzm16t5/osXkpfm5R8+OJ1Ur4uZxRmaFigiA6azxUnkpXrxOB3qdS0iMgKs2t1IQbqXcTnJp/zcFzZVU9/eTSAUBuCSaQWDXZ6IJABHvAsY7hwOQ1GmT+34RESGuUPzrReU55xWh44XNtUwuyRj0C+EFJHEonA9AMWZSVQ2aSEZEZHhbF9jJ9WtfhaexpSQ6hY/6/c3c8WMwhhUJiKJJGbh2hjziDGm1hiz8Tj7P26M2RD9essYM6fPvj3GmPeNMeuMMatjVeNAFWcmaVqIiMgwt3J3ZL716YTrlzZXA3DFDE0FEZEzE8uR68eAK0+wfzew2Fo7G/g+8NBR+5daa+daa+fHqL4BK8lKpratm+5gKN6liIjIcazc1Uh2iue0VkI8qzSLey6dxMR8dYUSkTMTswsarbUrjDFlJ9j/Vp+H7wAlsarlTBVHO4YcbPZTlpsS52pERKQ/q/Y0sKAs+7TmW88szmBmcUYMqhKRRDNc5lx/Bni+z2MLvGiMWWOMuSNONfXq7XWtixpFRIalquYu9jd2nVYLvg2Vzaze04i1NgaViUiiiXu4NsYsJRKuv95n8yJr7dnAVcBdxpiLTvD8O4wxq40xq+vq6mJS46Fe15p3LSKJyBhzpTFmmzFmhzHmG8c5Zkn0OplNxpjXhrrGVYfmW48/9XD981d2cNcTa1G2FpHBENdwbYyZDTwMXGutbTi03VpbFb2tBZ4CjrsMlrX2IWvtfGvt/Ly8M1/utj+FGT4cRgvJiEjiMcY4gZ8TGeyYDtxsjJl+1DGZwC+AD1lrZwAfHeo6V+5uJM3nYmph+ik9r6snxGvb67h8eiEOLV8uIoMgbuHaGFMKPAl80lq7vc/2FGNM2qH7wOVAvx1Hhorb6aAg3ad2fCKSiBYAO6y1u6y1PcAy4NqjjrkFeNJauw96B0aG1MrdDZxTlo3zFAPy6xV1+ANhteATkUETswsajTG/A5YAucaYSuC7gBvAWvsg8A9ADvCL6MUnwWhnkALgqeg2F/CEtfYvsapzoNSOT0QSVDGwv8/jSmDhUcdMBtzGmFeBNOB+a+3jQ1Me1LV1s6uugxvnjz3l576wqYZ0n+u0ppOIiPQnlt1Cbj7J/r8B/qaf7buAOcc+I75KspJYvbcp3mWIiAy1/oaCj56d7ALmAZcAScDbxph3+n4q2ftikYvU7wAoLS0dlAIPzbc+1YsZrbW8u6eRS6YV4HbG/RIkERklYhauR5virCSe3XCQUNie8seOIiIjWCXQd0i4BKjq55h6a20H0GGMWUFkkOSYcG2tfYjougbz588flEsIV+1uINnjPOVWesYYln95MW3+wGCUISICDINuISNFcWYywbClptUf71JERIbSu8AkY0y5McYD3AQ8fdQx/wdcaIxxGWOSiUwb2TJUBa7c3ci8cVmnNfrscTnISfXGoCoRSVQK1wN0aCEZ9boWkURirQ0CdwMvEAnMv7fWbjLGfM4Y87noMVuAvwAbgFXAw9baIbkQvbmzh63Vbae85Lm1lo8//A5PvVcZo8pEJFFpWsgA9S4k09TFOWXxrUVEZChZa58Dnjtq24NHPf4x8OOhrAvg3T2Ra2EWlOec0vPeP9DCmzsauG5ucSzKEpEEppHrAToUrtWOT0Rk+Fi5qwGPy8HsklObb/3iphqcDsOl0wpiVJmIJCqF6wFK8jjJSfFoWoiIyDCyak8jZ43NxOd2ntLzXthUzYKybLJSPDGqTEQSlcL1KSjJSqJSva5FRIaFNn+AjQdaTnm+9a66dipq27lihkatRWTwKVyfguKsJI1ci4gME2v2NhG2sHD8qc23DoUtH5hVxOValVFEYkDh+hQUZyZR1dyFtYPSmlVERM7Ayt2NuByGs0ozT+l5kwrS+PnHz2ZM9FoaEZHBpHB9Coozk/AHwjR09MS7FBGRhLdqdyOzSzJI9gy88VVLZ4C9DR0xrEpEEp3C9SkYl5sCwPbqtjhXIiKS2Lp6QmyobD7lFnxPrz/A4h+/yu56BWwRiQ2F61NwTlk2bqdhRUV9vEsREUlo7+1rIhCyp3wx44ubaxifm0JZTnKMKhORRKdwfQpSvS7mj8vmte118S5FRCShrdzdiMPAvLKsAT+npTPA2zsbuHxGIcaYGFYnIolM4foULZ6Sx5aDrdS0+uNdiohIwlq5u4HpY9JJ97kH/Jy/bqshGLZcrhZ8IhJDCtenaPHkPACNXouIxEl3MMR7+5pZeIrzrV/cVEN+mpe5JZmxKUxEhAGGa2NMijHGEb0/2RjzIWPMwIcLRpGphWnkp3kVrkVE4mRDZQvdwTALTnG+9T9fP4sHPzkPh0NTQkQkdgY6cr0C8BljioGXgduBx2JV1HBmjGHx5DzeqKgnGArHuxwRkYSzancjELnI/FRkpXg4u3Tgc7RFRE7HQMO1sdZ2Ah8G/sNaez0wPXZlDW9LpuTT0hVgfWVLvEsREUk4K3c3MqUgjewUz4Cf86vXdvI/7+6LYVUiIhEDDtfGmPOAjwN/jm4beNf+UeaCibk4jOZdi8jIY4y5yxiT2edxljHm83Es6ZQEQ2HW7Gk8pSkhwVCYX762k7d3NsSwMhGRiIGG63uAbwJPWWs3GWPGA6/ErKphLiPZzVmlWQrXIjISfdZa23zogbW2Cfhs/Mo5NZuqWunoCbFw/MDD9ao9jTR3BrhiRmEMKxMRiRhQuLbWvmat/ZC19l+jFzbWW2u/EOPahrXFk/PYUNlMo5ZCF5GRxWH6NHk2xjiBgc+viLOVuyOjz6cycv3iphq8LgeLp+TFqiwRkV4D7RbyhDEm3RiTAmwGthljvhrb0oa3xZPzsBZer9DotYiMKC8CvzfGXGKMuRj4HfCXONc0YKt2NzI+N4X8NN+AjrfW8uKmai6clEeyJ2FnM4rIEBrotJDp1tpW4DrgOaAU+OSJnmCMecQYU2uM2Xic/cYY84AxZocxZoMx5uw++640xmyL7vvGAGscUrOKM8hO8fDaNoVrERlRvkqk69OdwF3R+1+La0UDFApbVu0+tfnWzZ0BSrKSuWqmpoSIyNAY6K/x7mhf6+uAn1lrA8YYe5LnPAb8DHj8OPuvAiZFvxYCvwQWRj+i/DlwGVAJvGuMedpau3mAtQ4Jh8Nw4aRcVlTUEQ5b9U0VkWEvOq1vg7V2JvBgvOs5VfsaO+nsCZ1SuM5K8fD7z50Xw6pERI400JHrXwF7gBRghTFmHNB6oidYa1cAjSc45FrgcRvxDpBpjCkCFgA7rLW7rLU9wLLoscPO4sl51Lf3sPngCf8oRESGBWttGFhvjCmNdy2nozw3hQ33Xs5VM4sG/Jw2fyCGFYmIHGugFzQ+YK0tttZeHQ3De4GlZ/i9i4H9fR5XRrcdb/uwc5GWQheRkacI2GSMedkY8/Shr3gXNVDJHhdJHueAjq1v7+asf3yJZ9ZXxbgqEZHDBjQtxBiTAXwXuCi66TXgH4EzWUWlv3kU9gTbj1fbHcAdAKWlQzsYk5vqZVZxBq9tq+OupROH9HuLiJym78W7gKGyv7GTYNiS5B5YGBcRGQwDnRbyCNAGfCz61Qo8eobfuxIY2+dxCVB1gu39stY+ZK2db62dn5c39G2WFk/OY82+Jlr10aOIDGPGGJ8x5h7go8BU4M1om9XXrLWvxbe62DjQ3AVAcVZSnCsRkUQy0HA9wVr73eg86F3W2u8B48/wez8NfCraNeRcoMVaexB4F5hkjCk3xniAm6LHDkuLp+QRClve2lEf71JERE7k18B84H0iF5T/W3zLib0DTQrXIjL0BtotpMsYc4G19g0AY8wioOtETzDG/A5YAuQaYyqJTCtxA1hrHyTS0u9qYAfQCdwe3Rc0xtwNvAA4gUestZtO8X0NmbPGZpLmc/Ha9jquPIWLbEREhth0a+0sAGPMfwGr4lxPzFU1d5Hmc5Huc8e7FBFJIAMN158DHo/OvQZoAm490ROstTefZL8l0mO1v33PEQnfw57L6eCCibm8tq0Oay19Fj4TERlOeueuRQcx4lnLkFhQnkNWyohZfFJERokBhWtr7XpgjjEmPfq4NTp3b0MMaxsxFk/O4/mN1VTUtjO5IC3e5YiI9GeOMeZQ31ADJEUfGyLjHenxKy02PjC7iEhzFBGRoTPQOddAJFRHV2oE+HIM6hmRelvyabVGERmmrLVOa2169CvNWuvqc3/UBWuAmlY/4fDJ1jsTERlcpxSujzL6P1McoDGZSUwuSFW/axGRYaLVH2DhP7/Mw2/sincpIpJgziRcazigjyVT8lm1u5HOnmC8SxERSXi9nUIyk+NciYgkmhOGa2NMmzGmtZ+vNmDMENU4IiyenEdPKMw7uxriXYqISMJTGz4RiZcThutDc/H6+Uqz1g6000hCmF+WRZLbqXnXIiLDwKEFZMZk+uJciYgkmjOZFiJ9eF1Ozp+Qo3nXIiLDwIHmLjwuB7kp3niXIiIJRqPPg2jxlDxe3lrLnvoOynJT4l2OiEjCumRqPmMyfDgcuvZeRIaWwvUgWhxtybeiok7hWkQkjhaOz2Hh+Jx4lyEiCUjTQgbRuJwUynKSeVXzrkVE4mpDZTPNnT3xLkNEEpDC9SBbPDmPt3c24A+E4l2KiEhC8gdCfOhnb/Lrt/bGuxQRSUAK14Ns8ZQ8ugIhVu9pincpIiIJqbrFD6gNn4jEh8L1IDt3fA4el4PXttfGuxQRkYR0qA1fcabCtYgMPYXrQZbscbGwPFst+URk1DDGXGmM2WaM2WGM+cYJjjvHGBMyxtwwlPUd7dACMiUauRaROFC4joHFk/PYXtNOVXT0RERkpDLGOIGfA1cB04GbjTHTj3PcvwIvDG2Fx6ps7sIYKMzQAjIiMvQUrmOgtyWfRq9FZORbAOyw1u6y1vYAy4Br+znu/wF/BOI+J+6a2UXcf9NZuJ36ESciQ09nnhiYmJ/KmAyfpoaIyGhQDOzv87gyuq2XMaYYuB54cAjrOq5JBWl8aM6YeJchIglK4ToGjDEsnpLHGxX1BELheJcjInIm+lvi0B71+KfA1621J+1Baoy5wxiz2hizuq4uNgMQf91aw576jpi8tojIyShcx8jiyXm0dQd5b19zvEsRETkTlcDYPo9LgKqjjpkPLDPG7AFuAH5hjLmuvxez1j5krZ1vrZ2fl5c36MWGwpY7Hl/D/6zef/KDRURiQOE6Rs6fmIvTYdSST0RGuneBScaYcmOMB7gJeLrvAdbacmttmbW2DPhf4PPW2j8NeaVAbZufYNgyRm34RCROFK5jJN3nZl5pluZdi8iIZq0NAncT6QKyBfi9tXaTMeZzxpjPxbe6Y/W24VO4FpE4ccXyxY0xVwL3A07gYWvtD4/a/1Xg431qmQbkWWsbox8vtgEhIGitnR/LWmNh8ZQ8fvzCNmpa/RSkqyWUiIxM1trngOeO2tbvxYvW2tuGoqbj6V1ARj2uRSROYjZyPZDeqNbaH1tr51pr5wLfBF6z1jb2OWRpdP+IC9YAV80sxOUw/OvzW+NdiohIQtDqjCISb7GcFjLQ3qiH3Az8Lob1DLnxeancuWQCT753gFe2ae61iEis3TCvhCc+u5AUb0w/mBUROa5YhuuT9kY9xBiTDFxJZAGCQyzwojFmjTHmjphVGWN3XzyRifmp/P2T79PmD8S7HBGRUS0/zcf5E3LjXYaIJLBYhuuB9EY95BrgzaOmhCyy1p5NZFrJXcaYi/r9JkPQM/VMeF1O/vUjsznY6udHf9kW73JEREa1P6zez5q9TfEuQ0QSWCzD9UB6ox5yE0dNCbHWVkVva4GniEwzOUase6YOhnnjsrj9/HL++529rNzVEO9yRERGJWst9z69iWc3HO9HjYhI7MUyXJ+0NyqAMSYDWAz8X59tKcaYtEP3gcuBjTGsNea+csVkxmYn8Y0n38cfOOkiZiIicopaugJ09IR0MaOIxFXMwvUp9Ea9HnjRWtt3rdoC4A1jzHpgFfBna+1fYlXrUEj2uPjhh2ezu76Df1++Pd7liIiMOpWHelyrDZ+IxFFML6ceSG9Ua+1jwGNHbdsFzIllbfGwaGIuN84fy3+u2MUHZhUxuyQz3iWJiIwah9rwaXVGEYknrdA4xL71gWnkpnr52v9uoCcYjnc5IiKjxqHVGTUtRETiSeF6iGUkufmn62aytbqNX722M97liIiMGrcsLOWvf7eY7BRPvEsRkQSmcB0Hl88o5IOzi/iPv+6goqYt3uWIiIwKPreT8XmpGNNfJ1gRkaGhcB0n935oBileJ1/74wZC4eO1/xYRkYF6+PVdvLCpOt5liEiCU7iOk9xUL9+9Zgbv7Wvmsbf2xLscEZER75ev7uTVbbXxLkNEEpzCdRxdO3cMS6fkcd8L29jX0BnvckRERix/IERDR48uZhSRuFO4jiNjDD+4fhZOh+GbT23AWk0PERE5HYfa8BWrx7WIxJnCdZyNyUziG1dN5c0dDfx+9f54lyMiMiIdbsOXHOdKRCTRKVwPA7csKGVheTb/9Oct1LT6412OiMiIU9vWDWjkWkTiT+F6GHA4DD/8yGx6gmG+/aeNmh4iInKKbphXwpZ/vJKidF+8SxGRBKdwPUyU56bwd5dP5qXNNfx0eYUCtojIKUryOHE41ONaROLLFe8C5LDPXDCe7TXt3P9yBZ09Qb519TQthiAiMgA/eXEbeek+PnnuuHiXIiIJTiPXw4jTYfjRR2bzqfPG8Z+v7+bbf9pIWAvMiIic1B/XHmDt3qZ4lyEiopHr4cbhMHzvQzNI9rh48LWddPWE+NENs3E59XuQiEh/gqEw1a1+9bgWkWFB4XoYMsbw9SunkOp1ct+L2+kKhLj/prPwuBSwRUSOVtPWTShs1SlERIYFpbVhyhjD3RdP4jsfnM7zG6u5479X4w+E4l2WiMiwc7jHtcK1iMSfwvUw95kLyvnhh2fx2vY6bn1kFe3dwXiXJCIyrLR3B8hMdmvkWkSGBYXrEeCmBaX89Ma5rN7bxCceXklLZyDeJYmIDBsXTy1g3T9czoS81HiXIiKicD1SXDu3mF9+/Gw2V7Vy03++Q317d7xLEhEREZGjKFyPIJfPKOThW+ezu76dG3/1NtUtWipdROQfn9nMj1/YGu8yREQAhesR56LJeTz+6YXUtHbz0V+9xf7GzniXJCISV69tr2VXXUe8yxARAWIcro0xVxpjthljdhhjvtHP/iXGmBZjzLro1z8M9LmJbEF5Nr/9m4W0+YN89MG3eW17XbxLEhGJC2stB5q71ClERIaNmIVrY4wT+DlwFTAduNkYM72fQ1+31s6Nfv3jKT43Yc0Zm8myO87F63Zw6yOr+OR/rWTLwdZ4lyUiMqQaO3rwB8KMUbgWkWEiliPXC4Ad1tpd1toeYBlw7RA8N2FMLUznxS9dxHc+OJ0NlS1c/cDrfO1/12sutogkjAPN0R7XasMnIsNELMN1MbC/z+PK6LajnWeMWW+Med4YM+MUn5vwvC4nn7mgnBVfXcrfXFDOn96rYsl9r/CTF7epJ7aIjHqBkGV6UTrjcpLjXYqICBDbcG362WaPerwWGGetnQP8B/CnU3hu5EBj7jDGrDbGrK6rS9y5xxnJbv7+A9N5+e8Wc9n0Qh746w6W/PhVnli5j2AoHO/yRERiYt64LJ774oVMLUyPdykiIkBsw3UlMLbP4xKgqu8B1tpWa2179P5zgNsYkzuQ5/Z5jYestfOttfPz8vIGs/4RaWx2Mv9x81k89fnzKctJ5ltPvc9V97/OK1trsbbf309EREREZJDEMly/C0wyxpQbYzzATcDTfQ8wxhQaY0z0/oJoPQ0Dea6c2FmlWfzhc+fx4CfOJhAKc/tj7/KJ/1rJpqqWeJcmIjJovvHHDdyz7L14lyEi0ssVqxe21gaNMXcDLwBO4BFr7SZjzOei+x8EbgDuNMYEgS7gJhsZXu33ubGqdbQyxnDlzCIunlrAEyv3cv/LFXzggTdYPDmPT5w7joun5uN09DcDR0RkZHj/QAt5ad54lyEi0itm4Rp6p3o8d9S2B/vc/xnws4E+V06Px+XgtkXlXH92CY+9uYffrdrHZx9fzZgMH7csLOVj54wlP80X7zJFRE7ZgeYu5o7NjHcZIiK9YhquZXjJSHLzxUsncdfSCSzfUstvV+7lvhe389PlFVwxs5BPLBzHueOzic7UEREZ1jq6gzR3BtSGT0SGFYXrBORyOrhyZiFXzixkd30Hv31nL39YU8mfNxxkYn4qn1hYyofnlZDuc8e7VBGR4+rtca0FZERkGInp8ucy/JXnpvDtD05n5bcu4b6PziHF6+LeZzaz8Acv840/bmDjAV0AKSLDkwEunZbPxPzUeJciItJLI9cCgM/t5IZ5Jdwwr4SNB1r4zTt7+b91VSx7dz8T81O5emYhV88uYkpBmqaNiCQYY8yVwP1ELjB/2Fr7w6P2fxz4evRhO3CntXZ9rOuaVJDGw7eeE+tvIyJySsxo6n08f/58u3r16niXMWq0dAV4en0Vz204yMrdDYQtjM9N4apZhVw9q4jpRekK2iKDxBizxlo7P951HM0Y4wS2A5cRWYPgXeBma+3mPsecD2yx1jYZY64C7rXWLjzZa5/pOdtaq3OQiMTFic7ZGrmW48pIcvPJc8fxyXPHUdfWzYubq3n+/WoefG0XP39lJ+NykrlqZhFXzypkVnGGfsiJjE4LgB3W2l0AxphlwLVAb7i21r7V5/h3iCz8FXNf/v169jd28r93nj8U305EZEAUrmVA8tK8fHzhOD6+cBwN7d28tLmGP79/kP98fRcPvraTkqwkrp5VxFUzC5lTkolD/bNFRotiYH+fx5XAiUalPwM8H9OKovY1duJ26lwjIsOLwrWcspxULzctKOWmBaU0dfTw0uYantt4kEff3M1DK3aRlezm/Im5XBD9GpudHO+SReT09Zde+51PaIxZSiRcX3DcFzPmDuAOgNLS0jMq7EBTF4sm5p7Ra4iIDDaFazkjWSkePnbOWD52zlhaOgP8dVsNr1fU8+aOev684SAApdnJLIoG7fMn5JCV4olz1SJyCiqBsX0elwBVRx9kjJkNPAxcZa1tON6LWWsfAh6CyJzr0y0qEApT0+ZXj2sRGXYUrmXQZCS7uf6sEq4/qwRrLTvr2nmjop43djTwzPoqfrdqH8bAzDEZvWF7flkWPrcz3qWLyPG9C0wyxpQDB4CbgFv6HmCMKQWeBD5prd0+FEVVt/ixFkrU41pEhhmFa4kJYwwT89OYmJ/GbYvKCYTCbKhs4Y3oqPbD0bnaHpeDheXZXDQpj4sm5zG5IFUXRooMI9baoDHmbuAFIq34HrHWbjLGfC66/0HgH4Ac4BfR/7/BWHc+cToMnzx3HDOK02P5bURETpla8UlcdHQHWbW7kdcr6nm9oo6K2nYACtN9XDgpl4sm53HhpFwykzWFRBLDcG3FF0s6Z4vISKVWfDLspHhdLJ2az9Kp+QBUNXexYnsdKyrqeGFTNX9YU4nDwOySTC6anMfiybnMKcnE5dSioiIC7d1BvC4Hbp0TRGSYUbiWYWFMZlJvB5JgKMz6ypbesP2zv1bwwMsVpPtcLJqYy9yxmUwpTGNaUTr5aV5NIxFJQN9/ZjOvba/jnW9dEu9SRESOoHAtw47L6WDeuCzmjcviS5dNprmzhzd3NLBiex1v7Kjn+Y3VvcdmJruZWpjG1MJ0phamMSX6lezRP22R0exAcxeFGb54lyEicgwlEBn2MpM9fGB2ER+YXQRAc2cPW6vb2FbdxtbqVrZWt/H71fvp7AkBYEyk/V8kbKczc0w6c8dmkp+uH8Qio8WB5i6mF+liRhEZfhSuZcTJTPZw7vgczh2f07stHLZUNnWxpbr1iND90uYawtFrdgvTfcwuyWDO2EzmlGQyqySDjCR3nN6FiJyucNhyoLmLy6YXxLsUEZFjKFzLqOBwGEpzkinNSeaKGYW927t6Qmw+2MK6/S1sqGxmQ2ULL26u6d1fnpvCnJIMZpdkMmdsBjPGZKjvtsgwV9/RTU8wTLF6XIvIMKRwfRI7atvZ19hBfVsPde3dtPoD5KV6+ZsLxwOwZm8jnT0hUr0u0nwuUr1u0pNcmvM7TCR5nMwbl828cdm921o6A2w4EAna6/c3886uRv60LrLgnNNhyE/zkpfmJTfVS26qp8/9w9vz0ryk+1y6mFIkDjxOB1+7cgoLyrNPfrCIDLpAIEBlZSV+vz/epcScz+ejpKQEt3vgn3QnfAJ8YVM1b+9soL69O/rVg7WWl/9uCQA/+svWI0Y6PU4HJdlJveH6R3/ZxsrdjUe85owx6fz5CxcCcMfjq2nvDlKWm0J5TgrluSlMLkijNCd5aN6gHCMj2c2Fk/K4cFJe77aaVj/r9zez8UALVS1+6tq6qWn1s/FACw0dPYTCx/aD9zgd5KV5mZifylmlmcwdG/lSb26R2MpM9vD5JRPjXYZIwqqsrCQtLY2ysrJRPchkraWhoYHKykrKy8sH/LyED9dv7ajnybUHyE2LjFJOyk+lIN2HtRZjDPdcOpnPLZlAXnTkMsnjJNwnaP3wI7NpaO+mrTtIuz9Imz9Iqu/wH2t5bgqr9jTy3PsHae4MALB0Sh6P3r4AgC//fh3pPjfluSmU5aYwPjeF4swkHI7R+491OCpI93H5jEIu7zOl5JBw2NLcFaCuLfILWN/b2rZuNle1cv/LFRxaj6k8N6U3aM8dm8m0onQ8LvXiFRks1S1+QtYyJsM3qn+wiwxXfr9/1AdriKw2nZOTQ11d3Sk9L+HD9XevmcH3rp153P3Txxx7NXrf4FueGxmNPp5vXj2t935zZw+76ztwRp8fClt21Lazs7adjminC4BPnFvKP103i3DY8uR7B5hWlMak/DQFtDhxOAzZKR6yUzxMIa3fY9q7g2yobGbd/mbe29fMGzvqeeq9AwB4XA5mRDuWzB2byYwx6YzLSdHiFyKn6Zev7uDJ9w7w/r1XxLsUkYQ12oP1IafzPmMaro0xVwL3A07gYWvtD4/a/3Hg69GH7cCd1tr10X17gDYgBARjtSzwUI4QZyZ7OKv08JQBp8Pw9N0XYK2lrr2bPfWd7Kxr7w3rexs7+cof1gPgdhom5acxY0w6Ny8s5ezSrCGrW04u1evi/Am5nD8hF4h8lFTV4mfdvmbW7W9i3f5mfrdqH4++uQcAl8NQnpvCpIJUJuWn9d6W5SbjdemCSpETOdDcpYsZRRJYQ0MDl1wSWUCquroap9NJXl5kqueqVavweI4/PXP16tU8/vjjPPDAAzGrL2bh2hjjBH4OXAZUAu8aY5621m7uc9huYLG1tskYcxXwELCwz/6l1tr6WNU4XBhjyE/zkZ/mO+ICndLsZF7+u8VsrmplU1Urm6pa+OvWWi6ZFlky/N09jXzlD+uZWZzB7OIMZhVnMKNY7eWGA2MMxZlJFGcm9fbnDoTCbKtuY3tNGxW17VTUtLO5qpXnN1b3TilxOgzjcpKZlH84dBdnJvVeSJniTfgPm0SobOqiJEvhWiRR5eTksG7dOgDuvfdeUlNT+cpXvtK7PxgM4nL1//Ny/vz5zJ8fk/HaXrH8Sb0A2GGt3QVgjFkGXAv0hmtr7Vt9jn8HKIlhPSOO02GYkJfKhLxUrpkzBoiMiB4KYh6ng+lF6azf38yfNxzsfd4zd1/ArJIMdta1U9vazczidNJ8Ctzx5nY6mFmcwczijCO2+wMhdtV1UFHbxo5o6K6obWP5ltpjLqRMcjvJTfP0di/JTfWS17ejSZqXogwfRRlJvdOPREabA81dLFSnEBHp47bbbiM7O5v33nuPs88+mxtvvJF77rmHrq4ukpKSePTRR5kyZQqvvvoq9913H88++yz33nsv+/btY9euXezbt4977rmHL3zhC2dcSyzDdTGwv8/jSo4clT7aZ4Dn+zy2wIvGGAv8ylr70OCXOPIYYzg0/WfO2Ex++Yl5ADR29LDxQAvvH2hhYn4qAH9YXcmDr+0EYHxuCrNKIqPbnzqvTPO3hxGf28n0MenHzO/vDobY29BJVXMX9e09kW42bYe72uxr6GTt3iYaO3t6f+E6xO00lGQlMzY7mXHZyZRmR+6XZkd6gadqBFxGqFZ/gDZ/kGKNXIvIUbZv387y5ctxOp20trayYsUKXC4Xy5cv51vf+hZ//OMfj3nO1q1beeWVV2hra2PKlCnceeedp9R2rz+x/Anb37DZsf3MAGPMUiLh+oI+mxdZa6uMMfnAS8aYrdbaFf089w7gDoDS0tIzr3qEyk7xcNHkPC6afLi93B0XjWfh+Gw2Vraw4UALK3c18uq2Oj5zQaSdzI9f2Mrehk5mRUdTZ47JICNZI9zDhdflZHJBGpML+r+I8pBgKExjZ09vL/aq5i72NnSyv7GTfY2drNvXRKs/eMRzclI8keCdEw3e0SBempNMYbpPo94ybLkdDn5641xm9HOxuYgMve89s4nNVa2D+prTx6Tz3WtmnPLzPvrRj+J0Rq5bamlp4dZbb6WiogJjDIFAoN/nfOADH8Dr9eL1esnPz6empoaSkjObSBHLcF0JjO3zuASoOvogY8xs4GHgKmttw6Ht1tqq6G2tMeYpItNMjgnX0RHthwDmz5/fb3hPVNkpHpZOyWfplPzebS1dgd4rX7sDYd7b18yzfaaUXDgpl//+TOQDhvcrWyjOSiI7RX2bhzOX09E7Z/94WjoD7IuG7b2NHb3Be83eJp5ZX0Xf2Sd9R71Ls5MYm9Vn5DsnmXRNMZI4SvI4ue6s4niXISLDUErK4e5t3/nOd1i6dClPPfUUe/bsYcmSJf0+x+v19t53Op0Eg8F+jzsVsQzX7wKTjDHlwAHgJuCWvgcYY0qBJ4FPWmu399meAjistW3R+5cD/xjDWhNG34sdv/3B6Xz7g9Np7OhhU1VkSklydOlvay23PbqKho4eCtN9TCtKY1pROhdNzuPc8TnxKl9OU0aym1nJGcwqyThmXyAU5mCzvzd872s8POq9obK5tz/7IV6XgzSfizSfO3rrIs3rJvXQfZ+b9D73M5PdkfCf7iXNq1Ut5czsru+gsaObs8ZmaT0AkWHgdEaYh0JLSwvFxZFfxB977LEh/d4xC9fW2qAx5m7gBSKt+B6x1m4yxnwuuv9B4B+AHOAX0R+4h1ruFQBPRbe5gCestX+JVa2JLjvFc8yKhdbCf9x8FhurWthysI0tB1t5vaKeQCjMueNz8AdC3PTQO0wtjITuaUXpTC1K06jmCOR2OijNST7uqqEtXQH29wncjR09tPqDtEXnvrb5IwvstEUXUWrvPv5v/T73oVF2L/npXvLTfOSleaOPfb1Lz2clezQ1Rfq1LNrScuv3r4x3KSIyjH3ta1/j1ltv5Sc/+QkXX3zxkH5vY4++EmoEmz9/vl29enW8yxi1uoMh/IEwGUluqlv8fOl/1rGluvWIkc0fXD+Tjy8cR0N7N8u31DAxP5WJeWmay51AQmFLe3ckZLf5AzS291Db1k1tm5/a1u7D99u6qWuNrG56NGMgO9lDTqqHnBQvOamRDik5KR5yUr1kp3jITY3cT/O58LgceJyRr5E6mmmMWROrfv7D1emcs+96Yi2bDrTw6leXxqgqETmZLVu2MG3atJMfOEr0935PdM5WywAZMK/L2bvASWGGj9/dcS7WWmpau9lysJXNB1uZNy6yuM36yma+/sf3e5+bm+plQl4K3/ngdGYWZ9DSGaCjJ0iRli8edZwOQ0aSOzoF6eQdHbp6Qr1hu7a1m4aOSDeUhvZuGtp7aOiILDFf3959zIWZx/v+HqcDt9PgcTnxOA0elwO304HH5cDndpLscZLqdZHscZHqdZLidUW+PH3uRx+n+dykJ7lI97lJ9jj17zXOqpq71ClERIY1hWs5I8YYCjN8FGb4WDr18IWTiyfn89pXl7Czrp0dtYe/fNE53c++X8XfP7WRFI+T8rwUynNTKc9N4dOLyshM9hAO2xE7AimnJsnjZFxOCuNyUk56bHcwRFNHgPr2bho6eqhv66ajJ0hPMExPKExPMEyg99b2sy1MVyBEmz9IdYufju4gHT0hOrqDBMMn/xTP5TCkJ0XmlGckuaP3o7fRAF6U4ePDZ6tlf6wcaOpiyZS8kx8oIhInCtcSE5GVBiOB6eKpBcfsP298Dt+/dgY7atvZ3dDJuv1N/HlDVW+bwJ+8tJ1l7+5nfG4KZbnJveH7sukFmoubwLwuJ4UZTgozjt8Z5XRYGwniHd2haOAORm67I0G81R+gtStAqz9AS1eA1q5g5NYfoKq5i5auIK1dAXpCYSbkpShcx0h3MERtWzfFmf1fHyAiMhwoXEtcjM9LZXxe6hHbuoOh3mkns0syqGvrZnd9B3/dWkd9eyUpHicbv3cFAN9/djMbKpsZG+3RXJqdTHleCmeXZg35e5GRzxjTO+3pTFpP+gMhunpCg1iZ9OUwhv+549xB/+VKRGQwKVzLsHEoWANcPqOQy2cU9j5u9QeobvH3znfNSfXgMIaVuxp5quUA1sLE/FSWf3kxAF/5w3oa2rspzU6mOCuJMZlJjM9NPWYVRJHB5HM7e6c+yeBzOx0sVCtQERnmFK5lREj3uY9o8/f5JRP5/JKJQGTEu6rZT3ufi918bgc1rd2s3tPU242i7wI5Nz/0DhbLmMwkijMj4XvGmHRml2QO3ZsSERGRUUfhWkY8r8tJee6RF8P903Wzeu+3+gMcaOo6Yn9xVhJ7Gzp4Z2cD1a1+whZunD+W2TdkEg5bzvnBcrJTPJGLNdN9FGX4WDQxl4Xjc7DW0tQZICvZrc4RIiIiQ2zJkiV885vf5Iorrujd9tOf/pTt27fzi1/8ot/j77vvPubPH5pupwrXMuql+9ykFx3ZZ/u+j87pvR8Mhalp6+ZQTO4JhblmzhiqW/wcbPWzvaaOurZuHA7DwvE51Lf3cM4PluNxOShMjyyCkpPi4eaFpSydkk+bP8CK7fW9vZlzUz1kJCmIi4iIDIabb76ZZcuWHRGuly1bxo9//OM4VnWYwrUkPJfTQXHm4b65PreTez905HKuwVC4t1Wbx+Xgu9dMj4TvFj/17d3saeigJbqYzp76Tu56Yu2R38Nh+MmNc/nQnDFsr2njgZcryEx2k5XsITPZQ1aymwsm5pKf7sMfCNEdCJPmc6kdoYiIyFFuuOEGvv3tb9Pd3Y3X62XPnj1UVVXxxBNP8KUvfYmuri5uuOEGvve978WlPoVrkQFwOR0cut4yI8nN7YvKj3vspIJU/nLPhTS091DffnhBlEn5ke4ozZ0BNlW10tTZQ0tXgEOLpP7mMwvJT/fx1621fP63a3EYyEz2kJnkJiPZzb9+ZDaTC9JYv7+Zl7fWkpnkJjM58pWR5GbGmAx8bifWWo2Si4jIqJWTk8OCBQv4y1/+wrXXXsuyZcu48cYb+eY3v0l2djahUIhLLrmEDRs2MHv27CGvT+FaZJD53E6mFh6/K8mC8mxe+coSILJUeJs/QFNngIJ0LwBTC9P49gem0dwZ6A3gLV0BvC4HABsOtPDAyxXHvO6Kry6lNCeZX63YxX+8XEFmcmQ6yqHw/a83zCbd52bN3kYqatqj2yPHpPlclGQlKZSLiMgpu/FXbx+z7YOzi/jkeWV09YS47dFVx+y/YV4JH50/lsaOHu78zZoj9v3P35530u95aGrIoXD9yCOP8Pvf/56HHnqIYDDIwYMH2bx5s8K1SKJxOkxkdDr5cG/l/nqA9/XJc8dxy4JS2vwBmjsDNEfDd340nM8uzuCmBaU0dwZo6eqhuTPAjtp2PM5IOH92w0EefXPPMa+785+vxmkiPcSff/8gab5I6E5PcpOb6uFHN0Tmqb+8pYaq5i5SfS7SvG5SfS4yk929v1AEQ2GcDqOgLiIiMXPdddfx5S9/mbVr19LV1UVWVhb33Xcf7777LllZWdx22234/f641KZwLTIC9RfKDzl/Yi7nT8w97nO/fuVUPnvh+Egw7+yh1R+gsyfUu/Ll9KJ0WroCtPkDtPmD1Lb5aezo6X3+snf389LmmiNec2x2Eq9/7WIAbn/sXd7Z1UCq10Waz02K18XUwjT+/ca5APz8lR3UtXWT4nWS7HGR6nUxNjupdyXPTVUtACR7XCS5nSR5nCS5nXiiI/ciIjK8nGikOcnjPOH+7BTPgEaqj5aamsqSJUv49Kc/zc0330xrayspKSlkZGRQU1PD888/z5IlS075dQeDwrVIgvG5nYyJ9vbuz0fmlfCRecdfvvs/bj6LNn+Q9u4g7f4gbd2BI/ZfO7eYmcUZkX3+AO3dIVK9h0817+xqYP3+Zjp6QoSiF4mePyGnN1x//rdr2dvQecRrXjotn4dvPQeAq+9/nc6eID63k2RPJHxfNCmPv108AYB/enYzDofB546E8iS3g+ljMlhQno21ltcr6nsDe2TRFweZyZ4jahQRkeHv5ptv5sMf/jDLli1j6tSpnHXWWcyYMYPx48ezaNGiuNWlnyYickoOrUKYl+btd/8NJwjmQO9CPtZauoNhOrqDRDM2AP/6kdk0dwbwB0J09oTo7AlSkpXcu39BeTZNnT109kSWGu8KRL4O+dO6Kjq6g0dsu+38MhaUZ9MTCvOpR46d+/f5JRP42pVTaeroYeG/vIzP5TgigH/6gnI+Nn8sdW3dfP2PG/C6HHhdDjwuB16Xk2vmjGFBeTb17d14XI4jFjwSEZHYuP7667H28A+Qxx57rN/jXn311aEpKErhWkTiwhjT73Lh555keeuj2yQebfW3LwUOh/eunhBOZ2TKi8vh4H8/dx7+QJiuQAh/NJhPKUiL7HcaPr2oHH+fff5AqDcs94TC1Lb56QmG6Q6Ge29nlURGxg82+ylI9ypci4gkMIVrERmV+gvvTodhfln2cZ+T5nPzjaumHnd/cWYSz/6/C4+7f8aYdHQdp4hIYlO4FhEZJFr0R0REdPm9iIiIiJySvnOdR7PTeZ8K1yIiIiIyYD6fj4aGhlEfsK21NDQ04PP5Tul5mhYiIiIiIgNWUlJCZWUldXV18S4l5nw+HyUlJ+6CdbSYhmtjzJXA/YATeNha+8Oj9pvo/quBTuA2a+3agTxXRESGxpmcy0Vk9HG73ZSXl8e7jGErZtNCjDFO4OfAVcB04GZjzPSjDrsKmBT9ugP45Sk8V0REYuxMzuUiIokolnOuFwA7rLW7rLU9wDLg2qOOuRZ43Ea8A2QaY4oG+FwREYm9MzmXi4gknFiG62Jgf5/HldFtAzlmIM8VEZHYO5NzuYhIwonlnOv+Gr4efVnp8Y4ZyHMjL2DMHUQ+hgRoN8ZsG3CFEblA/Sk+Z6RLtPes9zu6jZb3Oy7eBRzHmZzLj32xMz9nw+j5Ox8ovd/RTe93ZDruOTuW4boSGNvncQlQNcBjPAN4LgDW2oeAh063SGPMamvt/NN9/kiUaO9Z73d0S7T3Gwdnci4/xpmesyHx/s71fkc3vd/RJ5bTQt4FJhljyo0xHuAm4Omjjnka+JSJOBdosdYeHOBzRUQk9s7kXC4iknBiNnJtrQ0aY+4GXiDSvukRa+0mY8znovsfBJ4j0rppB5H2Tbef6LmxqlVERPp3JudyEZFEFNM+19ba54icdPtue7DPfQvcNdDnxsgZfTw5QiXae9b7Hd0S7f0OuTM5l8dIov2d6/2Obnq/o4wZ7UtXioiIiIgMlVjOuRYRERERSSgJHa6NMVcaY7YZY3YYY74R73pizRizxxjzvjFmnTFmdbzrGWzGmEeMMbXGmI19tmUbY14yxlREb7PiWeNgO857vtcYcyD697zOGHN1PGscLMaYscaYV4wxW4wxm4wxX4xuH9V/x3KYztmjT6KdtxPpnA2Je95O2HCdwEusL7XWzh2lbXAeA648ats3gJettZOAl6OPR5PHOPY9A/x79O95bnS+7GgQBP7OWjsNOBe4K/p/drT/HQs6Z4/SczYk3nn7MRLnnA0Jet5O2HCNllgfday1K4DGozZfC/w6ev/XwHVDWVOsHec9j0rW2oPW2rXR+23AFiKrAI7qv2PppXP2KJRo5+1EOmdD4p63EzlcJ+JyvRZ40RizJrpKWiIoONRvN3qbH+d6hsrdxpgN0Y8gR9XHbQDGmDLgLGAlift3nGh0zk4cifh/elSfsyGxztuJHK4HvFzvKLLIWns2kY9V7zLGXBTvgiQmfglMAOYCB4F/i2s1g8wYkwr8EbjHWtsa73pkyOicrXP2aDWqz9mQeOftRA7XA16ud7Sw1lZFb2uBp4h8zDra1RhjigCit7VxrifmrLU11tqQtTYM/Cej6O/ZGOMmcoL+rbX2yejmhPs7TlA6Z4+i/8snkVD/p0fzORsS87ydyOE6oZZYN8akGGPSDt0HLgc2nvhZo8LTwK3R+7cC/xfHWobEoRNW1PWMkr9nY4wB/gvYYq39SZ9dCfd3nKB0zh4l/5cHIKH+T4/WczYk7nk7oReRiba7+SmHl/T9QXwrih1jzHgiIx8QWZnzidH2fo0xvwOWALlADfBd4E/A74FSYB/wUWvtqLmY5DjveQmRjxctsAf420Nz20YyY8wFwOvA+0A4uvlbRObvjdq/YzlM5+zR934T7bydSOdsSNzzdkKHaxERERGRwZTI00JERERERAaVwrWIiIiIyCBRuBYRERERGSQK1yIiIiIig0ThWkRERERkkChcS8IxxoSMMev6fH1jEF+7zBgzanqUiojEm87ZMtK44l2ASBx0WWvnxrsIEREZEJ2zZUTRyLVIlDFmjzHmX40xq6JfE6PbxxljXjbGbIjelka3FxhjnjLGrI9+nR99Kacx5j+NMZuMMS8aY5Li9qZEREYpnbNluFK4lkSUdNRHjDf22ddqrV0A/IzISnBE7z9urZ0N/BZ4ILr9AeA1a+0c4GxgU3T7JODn1toZQDPwkZi+GxGR0U3nbBlRtEKjJBxjTLu1NrWf7XuAi621u4wxbqDaWptjjKkHiqy1gej2g9baXGNMHVBire3u8xplwEvW2knRx18H3NbafxqCtyYiMuronC0jjUauRY5kj3P/eMf0p7vP/RC6tkFEJFZ0zpZhR+Fa5Eg39rl9O3r/LeCm6P2PA29E778M3AlgjHEaY9KHqkgREQF0zpZhSL+dSSJKMsas6/P4L9baQ62dvMaYlUR+8bw5uu0LwCPGmK8CdcDt0e1fBB4yxnyGyGjHncDBWBcvIpJgdM6WEUVzrkWiovP35ltr6+Ndi4iInJjO2TJcaVqIiIiIiMgg0ci1iIiIiMgg0ci1iIiIiMggUbgWERERERkkCtciIiIiIoNE4VpEREREZJAoXIuIiIiIDBKFaxERERGRQaJwLSIiIiIySBSuRUREREQGicK1iIiIiMggUbgWERERERkkCtciIiIiIoNE4VpEREREZJAoXIuIiIiIDBKFaxERERGRQRKXcG2MecQYU2uM2Xic/cYY84AxZocxZoMx5uyhrlFERCJ0zhYRGbh4jVw/Blx5gv1XAZOiX3cAvxyCmkREpH+PoXO2iMiAxCVcW2tXAI0nOORa4HEb8Q6QaYwpGprqRESkL52zRUQGbrjOuS4G9vd5XBndJiIiw4/O2SIiUa54F3Acpp9ttt8DjbmDyMeQpKSkzJs6dWos6xIRiYk1a9bUW2vz4l3HadI5W0QSyonO2cM1XFcCY/s8LgGq+jvQWvsQ8BDA/Pnz7erVq2NfnYjIIDPG7I13DWdA52wRSSgnOmcP12khTwOfil6Bfi7QYq09GO+iRESkXzpni4hExWXk2hjzO2AJkGuMqQS+C7gBrLUPAs8BVwM7gE7g9njUKSIiOmeLiJyKuIRra+3NJ9lvgbuGqBwRETkBnbNFRAZuuE4LEREREREZcRSuRUREREQGicK1iIiIiMggUbgWERERERkkCtciIiIiIoNE4VpEREREZJAoXIuIiIiIDJLhuvy5iMhx+QMh6tq6qWvvpjsQxut24HM58bodeF0OvC4nPnfk1u00GGMG9ftba2nvDtLSFej9au0K0toVwOU0fPjskkH9fiIiMnIoXIvIsBAOW5o6e6ht644E52h4rm2N3Na1+Xu3t/qDA35dY8DrcuBzO/G6HLidDjxOBy6nweVw4HYaXE4HLofB44rcupzR7Q4HobA9HKD9h4J0gLDt//uNzU5SuBYRSWAK1yIyYKGwJRS2OAw4jMEYTjoqHA5bGjt7qGn1U9vWTW2rn9rWbmraDt12UxfdF+wnsaZ4nOSleclL8zK1MJ0LJ0Xu56VGbr1uB93BMN2BMN3B0OHbYJjuYBh/IHo/cHhbIBQmGLIEw2ECfW47uoMEw5aeYJhg2BIMhXE6DBlJbnJSPYzPSyEjyU1Gkpt0X/Q2yU16kqt3e0aSO1Z//CIiMgIoXIskEGstPaE+oTMQptUfoLkzQFNnD02dAZo7oredPYe3RW9b/QHsUfnXGHAa0xu2HcYcEb47e0L9huasZDf5aT7y071MzMulIN1LfpqXvOi2/DQvualeUrw6TYmIyMihn1oicWStpakzQG10FLe2rbv3fl30fk8wDNHAaoiMFEfuRzYcun8o2B4Kz92BEP5ACH90JNcfCOMPho4Jx/1J8TjJTPaQleImK9nD2OxkspLdZCa58bqdhMOWsIWwtVhrCdm+jzlif7LHSUG6j/w0L/nR27w0Lz63M+Z/viIiIkNN4VokRqy1NHcGqGzqYn9TJ5VNnVQ2dXGwJTIFoq7VT117N4HQsWk3zesiLz0y9SEz2YONvp61YIkG2ENB1oK14eg+cEWnMfiiAdbnPjzfOPLYecQc5DSfm6xkN1kpHjKT3GQku/G6FHxFREROh8K1yADZ6OhsMHxovq4lEApT29odDc9d7G+M3B4K0u3dR154l+ZzUZThoyDdx4S8nMi0iDRvdBrE4fvJHv3XFBERGYn0E1wSlj8QYlddBxW1bVTUtFNR28aO2nY6ukORAB22hEKWQDhMKGz7HWE+WorHydjsZEqykjh3fA4lWUmUZCUzNjtyq4vdRERERjeFaxn1/IEQO+vaewP09pp2dtS2s7eho7edmtNhKMtJZmJ+KhlJ7t7WbC5HpGWb02FwOwxOx6EWbofbt+WleSnJSmJsVjKZye5B76ksIiIiI4fCtYwa1lpq27rZXNXK5oOtvbd9Q7TLYSjLTWFaURrXzBnDpPxUJhekUZabrHnGIiIicsYUrmVECoUtu+vb2dQ3SFe10tDR03vMuJxkphWm86E5Y5hckMakglTKclLwuBxxrFxERETirTsY6m1DOyk/Dadj8D51VriWYS0cthxo7mJnXTs76zrYUdvOloOtbK1uxR8IA+B2GiYXpHHx1HxmjEln+pgMphalke7T/GYREZHRzh8IUd/eTX17D40d3TR1RELzofDc3BVdr6Hj8LoNXYFQ7/NXf/tSclO9g1aPwrUMC/5AiN31Heysi8yH3lnXwc7adnbVt/eGaICMJDfTitK4ZcE4po9JZ8aYdCbkpWo0WkREZIQJhyNNAwIhSyC6em4gfPh+TyhMV08kONe199DQ3h0J0W090TDdTUN7D21HdeY6xGEgM9lDZnJkzYYxmT6mFaX3tp/NSIpsTxnkDl0K1zLkrLVU1LbzekU9b+9sYFtNK5VNXb2LmxgDJVlJTMhL5bwJOUzIS2VifioT8lLITvHogkEREZERIBS27KxrZ/3+ZjZUtrChspnd9R2RMB0K97t678lkp3jISfGQm+plVkkmuamR+4duc1K9kUXPkj2keV04BnG6x0ApXMuQqG3188aOet6oqOeNHfXUtnUDUJ6bwpySTD58Vkk0QKdSnptCkkcXF4qIiIwU1lr2N3axvrKZDZXNrK9sYeOBFjp7ItMvUr0uZhVncN1ZxfjcTtxOg9vpwO104HE6Io9dfR9HtvncTnJSPeSleslO8eByDv9PqhWuJSY6e4Ks3NXI6xX1vLmjnm01bUDkN85FE3O5YGIOF0zKozgzKc6VioiIyECFwpaaVj8Hmrs40BS5Jmp9ZQvvVzbT1BkAwONyMGNMOh+bP5bZJRnMLslkfG5KXEaR40HhWs6YPxBiT0MHu+s62F7Tzls761m7r4lAyOJxOVhQls31ZxdzwcRcphelJ8x/LhERkZGmOxjiYPPh8Fx56LapkwPNXVS3+I+YzuF0RJoKXDGjkNklmcwuyWBKYRruETDCHCsK1zIg1loOtvjZXd/Brmjnjl3R+weaD8+XBpgxJp1PX1DOhRPzmF+Whc+tKR4iIiLDQWdP8KjQ3BUN0pHwXNvWfcTPdGOgMN1HcWYS88ZlUZwZWXG4OCspej9JP+ePonAt/apr6+a17XW8UVHH9pp2dtd3HNG2JtnjZHxeCmeVZvGRs0sYn5fSO186xat/ViIiIvHS0N5NRW07FbXt7Knv4EA0QFc2dfZO3TjE7TQUZURC8kWT8npDc3FWEiWZyRRm+NSR6xQpBQkQmUO1vrKZV7fW8sq2Ot4/0AJAXpqX6UXpLByfzfi8VCbkpjA+L5WCdK+6doiIiMSJtZaa1m4qatuoqGlnR107O2raqahtOyJA+9yOyEhzZhKzSjJ6R5tLspIozkwmL807qAuoiMJ1Qmvs6GHF9jpe3VbLa9vraOoM4DAwb1wWX71iCkum5DG9KF0hWkREJE5CYUtlUyc7att7vypq29lZ235Ef+eMJDeTC1K5cmYhE/PTmJSfyqSCVArTffo5PsQUrhOItZaNB1p5ZVstr2yrZd3+ZqyFnBQPS6fms3RKPhdOyiUz2RPvUkVEZBhp9QdYuauRt3c20NTZw99/YBq5qV6Wb67hz+8fPOb47183k1Sviz9vOMjyLTXH7P/Xj8zG43Lw5NpKXq+oP2Kfwxj+7WNzAHhi5T7e3dN4xP4kj5N/vn4WAI++uZsNlS1H7M9MdvPda2YA8OBrO9lW3XbE/sIMH1+/cioA9y+vYE9DxxH7S7OT+dJlkwH40V+2crDFf8T+SQWpfH7JxMj7fHYzjR09R+yfVZzBpy8oB+Dvn3q/txXdIfPLsvj4wnEAfPUP63svDgyFLe3dQcZk+MhJ9bK9to03K+pp7w7Stx10mtfFrJIMPjiniM1VraT53KT5XHhdDowxLJ2Sz+UzCmns6OH7z27maNedVcziyXlUNXfx4xe2HbP/pnPGsnB8DnvqO7j/5Ypj9t96fhlzx2ayrbqNB1/becz+Oy4az7SidNbvb+axt/Ycs///XTyR8XmprNrdyO9W7Ttm/1eumEJxZhKvV9Tx5NoDx+wf7H97nzxvHGeXZh1z3JlQuB7lAqEwK3c18uLmal7aXMPBFj/GwJySTO65ZDJLpuQxqzhDHTxEROQYK3c18M/Pb+X9ymbCFrwuBwXpPnqCkZVzq1v9rNnbdMzzgqHI/qrmrn73WyJpcV9j5zH7XX1+Hu1t6Dhmf5rvcHTZVXfs/oL0w8tY76htP2Z/WW5K7/1tNa1sPNB6xP6uPmF488FWdtUdGb77/rR8/0AL1UeF7+Q+6zRsqGyhpevIOc7ZKR6217Sx5WArL22pwR8IEQzZIzpwGAMlmUn0hMKkeF29PZ/dTgefOHccn1s8gZauANf8xxvHzKE+qzQTgJ5guN8/+0UTc4FIp6/+9l82vQCA9u5gv/s/NHcMEPmFq7/9h95vc1f/+zu6I3++De3d/e4/9Odf29r//sH+t/fB2UXHHHOmjLWnvjrOcDV//ny7evXqeJcRdx3dQVZsr+OFTdX8dWstrf4gPreDxZPzuGx6IUun5JGT6j35C4nIkDHGrLHWzo93HUNJ5+zhIxAKs6Gymbd2NPDmzno+e+F4LplWwPuVLdz7zCYWTcjhvAm5nD0uE69LnSEGqrGjhy0HW6NfbWytbqWipp2eaAB0Ow3jc6OrEOdHbifmpTI+L0UdOIa5E52zNXI9StS3d/Pylhpe3FTD6zvq6QmGyUp2c8WMQi6fUcgFE3O16qGISByFwpb/XbOfA01d5KR6ufX8MgB+t2ofB5u7jji2MCOJWxaWAvD423uoj65qe0hpTgo3zCsB4OHXd9F61OjohPxUrp1bDMAvXt2B/6ipCdPHpHPlzCLa/AG+uGwdK3c10BE9ZnpROoFo+JtVksEf7zx/EN796Fff3s2q3Y2sr2yOBOmDrb2rEUOkQcDUwjRuW1TGtKI0phamMyEvVZ04RiGF6xGsrq2b/1t3gBc2VbN6bxPWQklWEp9YOI4rZhQwb1zWiFgmVERktGvq6OGL/7OOFdvrAJhSkNYbrv+4ppI1+478+Hru2MzecP3Eyn29q9wecsHE3N5w/eu391DZdGQ4v2J6YW+4fvj13TR1Hjkv+MNnlXDlzCJSvS7a/UGuO6uYRRNzOXd8Dtkpuu5mIGrb/Kzc1cg7uxpYubuRHbXtAHicDibmp3LBpFymFaYzrSidqUVp5OoT44ShaSEjjLWWt3c28NtV+3hxUzWBkGV6UTqXzyjg8umFTCtK01XBIiOQpoWMXjtq27n1kVXUtXVz74dmcPOCsTpPj0AHW7pYuauRlbsbWLmrkV31kbnYKR4n88uyWTg+m4XlOcwqztBodALQtJBRoLGjhz+uqeR3q/axq76DzGQ3nzqvjJsXlDIxPzXe5YmIyHEUpHsZn5fCzz9+NnPHZsa7HBkgay2vbq/j+fcP8s6uRvY1dgKRCyoXlGVz04KxLCzPYcaYdH1KLEdQuB7GrLW8u6eJJ1bu5bn3q+kJhZk/Lov/d8lErppZpIsdRESGKX8gxEMrdnHHReNJ87n5788sjHdJcgre2lnPfS9sY+2+ZjKS3Cwsz+bW88tYWJ7NtKJ0LboiJ6RwPQy1dAZ48r1Knli5j4radtJ8Lm5eMJZbFo5jSmFavMsTEZETqGzq5M7frOX9Ay3RRT0Gv9WXxMZ7+5q478VtvLmjgcJ0H/98/Sw+Or8Et0am5RQoXA8TgVCYt3Y28PS6Kp7dUEV3MMycsZn86COz+eCcIpI9+qsSERnuXttexxeXvUcoZPnPT83v7Rksw9uWg63824vbWb6lhpwUD9/54HQ+vrBUnxDLaVFii6PuYIg3Kup57v1qlm+poaUrQKrXxUfmlXDLglJmFmfEu0QRERmg/3l3H9948n2mFKTxy0/Mo7zPYiUyPO2qa+ffl1fw7IYqUr0uvnL5ZG5fVE6KV/FITp/+9QwxfyDEq9vqeH7jQf66pZa27iBpPheXTS/gqplFXDgpV78pi4iMQAvLc7jpnFK+88Fp+rRxmDvQ3MUDyyv437WVeJwO7lw8gb+9aAIZye54lyajQFz+9xtjrgTuB5zAw9baHx61PwP4DVBKpMb7rLWPDnmhg6SjO8gr22p5/v1qXtlWS2dPiKxkN1fPKuKqWYWcPyFXbXtEZNhKtHP2qdhU1cKf3jvAt66eRlluCv/y4VnxLklOoKq5i4dW7OKJlfsA+OS547hr6UTy0tSDWgbPkIdrY4wT+DlwGVAJvGuMedpau7nPYXcBm6211xhj8oBtxpjfWmt7+nnJYWt/Yyc/+PMWXtlWS3cwTG6ql+vPKubqWUUsLM9W6x4RGfYS6Zw9EG3+AG/tbOCtHfW8tbOBitp2CtK9/M2F4ylI98W7PDmKPxBi5e5GXt9ex4qKOrbXtON0GD46r4T/d8kkijOT4l2ijELxGLleAOyw1u4CMMYsA64F+p6oLZBmIl32U4FGIDjUhZ6JTVUt3Pbou/gDIW5eUMpVMwuZX5at9j0iMtIkxDn7eLp6Qqze20hZTgpjs5N5Z1cjf/vfa/C5HZxTls1H5pXw0Xkl5Gj1vWHBWsv2mnZer6jjte11rNrdSHcwjMflYEFZNjfMK+HKGUWU5iTHu1QZxeIRrouB/X0eVwJHNwD9GfA0UAWkATdaa8NDU96Ze6Oins/9Zg3pPhdP3Hk+kwrUPk9ERqxRf87uKxS2rN3XxFs7GnhzZz3v7WsiELJ89Yop3LV0IudNyOF/7jiXuaWZeF26PmY4aOro4fUd9by+vY7XK+qpbvUDMDE/lY8vHMdFk3NZWJ5Dkkd/XzI04hGu+xu6PXoN9iuAdcDFwATgJWPM69ba1mNezJg7gDsASktLB7fS0/Cn9w7wlT+sZ2J+Ko/dvoDCDH1MKCIj2qg+Zx8tEArz8YdXEgiFmTEmnU8vKuf8ibmcU5YFQKrXxcLxOXGuUmrb/Dy34SDPbDjI2n1NWAsZSW4umJjLRZNzuXBSHmM05UPiJB7huhIY2+dxCZHRjr5uB35orbXADmPMbmAqsOroF7PWPgQ8BDB//vyjT/hDxlrLr1bs4ofPb+W88Tn86lPzSPfpqmMRGfFG5Tn7eHxuJ//96QVMKUwjM9kT73Kkj5bOAH/ZdJCn11fx9s4GwhamFqbxxUsmcdHkPOaUZGrqpQwL8QjX7wKTjDHlwAHgJuCWo47ZB1wCvG6MKQCmALuGtMpTEApbvv/sZh57aw/XzBnDfR+drY8LRWS0GHXn7ON5a2c9OSlejUwPIx3dQZZvqeGZ9VW8tr2OQMhSlpPMXUsn8qE5YzTtUoalIQ/X1tqgMeZu4AUibZ0esdZuMsZ8Lrr/QeD7wGPGmPeJfCT5dWtt/VDXOhD+QIgv/34dz71fzWcvLOebV03Dod+cRWSUGG3n7BO59+lN5Kf5+M3fHD2lXIZSdzCyHsTT66t4eUsN/kCYwnQft51fxjVzxjCrOIPItbMiw1Nc+lxba58Dnjtq24N97lcBlw91XaeqpTPAZx9fzao9jXz7A9P4mwvHx7skEZFBN1rO2SdS3eJne007N8wriXcpCSkQCvPWzgaeWV/FC5uqafMHyU7xcMO8Ej40p5j547I0cCUjhpaQOk0Hmru47ZFV7G3o5D9uPotr5oyJd0kiInKaVlTUAXDhpLw4V5I4wmHLqj2NPLO+iuc3VtPY0UOa18XlMwr50NwxLJqQo/UgZERSuD4NW6tbue2Rd+noCfLrTy/gvAmanyciMpK9XlFPbqqXqYWawxtL1lrW7W/mmfUH+fP7VdS0dpPkdnLp9AKumV3ERZPz8Ll1zZKMbArXp+itnfX87eNrSPG6+MPnzmNqYXq8SxIRkTNgrWXlrgYumpSrubwxYK1ly8E2ntlQxbMbqtjf2IXH6WDJlDyumTOGS6blk+xRHJHRQ/+aT8G6/c3c9si7jMtJ5tefXqAemiIio4Axhr9+ZQnt/lGxqOSwsauunWfWH+Tp9QfYWdeB02FYNDGXL1w8ictnFJKRpHa1MjopXJ+C+17YRnpSZMRa/U9FREaPVK+LVK9+JJ6pA81dPLu+iqfXV7GpqhVj4JyybG5fVM5VMwu1TLwkBJ1JBmj1nkbe2FHP3189TcFaRGQUuffpTUwvSudj54w9+cFyjLq2bp57/yDPrK9i9d4mAOaMzeTbH5jGB2eP0UrFknAUrgfo/pcryE318PFzh99yvSIicnrau4P85p29aqV6ilo6A7ywqZqn11fx1s56whamFKTx1Sum8MHZRYzLSYl3iSJxo3A9AGv2NvJ6RT3funqqLroQERlF3tnZQDBsuWhybrxLGfYCoTAvba7hybUHeG17LYGQZVxOMp9fMpFr5oxhijqtiAAK1wPy0+UV5KR4+MS54+JdioiIDKIVFXUkuZ3MG5cV71KGraaOHp5YtY/fvLOXgy1+CtN93HpeZLXE2SVaLVHkaArXJ7FmbxOvV9Tzzas0ai0iMtq8XlHPueOz8brUW/loW6tbeezNPTz13gG6g2EWTczh+9fOZOnUfJxaLVHkuJQWT+L+lyvITvHwyfM0ai0iMpp09YQoyUri0ukF8S5l2AiFLS9vqeHRN/fw9q4GfG4HHz67hNsXlTG5QNM+RAZC4foE1u5rYsX2Or6hUWsRkVEnyePkvz+zMN5lDAut/gC/f3c/v357D/sbuxiT4eMbV03lpnPGqkOWyClSYjyB+5dHR60111pEZNTp6gmR5Enc6SDWWipq2/nNO3v53zWVdPaEWFCWzTevmsbl0wtwOR3xLlFkRFK4Po739jXx2vY6vn7lVFK0sICIyKgSDIU574cv8+lF5XzhkknxLmfIBENh1uxtYvmWGpZvqWV3fQcep4Nr5ozh9kVlzCzOiHeJIiOeUuNx3P9yBVnJbj6ludYiIqPO+spmmjsDTMhLjXcpMdfeHWTF9jqWb67hr9tqae4M4HE6OG9CDp++oJwrZxSSl6aVE0UGi8J1P9btb+bVbXV87copGrUWERmFVmyvxxhYNDEn3qXExMGWLpZvqWX55hre3tlATyhMZrKbi6fmc9m0Ai6cnKfl3kViRP+z+nH/8u3RUeuyeJciIiIx8HpFHbNLMkfVxXq1bX5+/+5+/rKpmo0HWgEoy0nm1vPHcdn0Qs4uzdQ8apEhoHB9lHX7m3llWx1fvWKKfqsXERmFWroCrNvfzN1LJ8a7lDNmrWXtviZ+/dZent94kEDIMm9cFt+4aiqXTitgQl6KFnkRGWJKj0e5f/l2MpPd3Hp+WbxLERGRGHAYuPdDM1hYPnKnhPgDIZ5eV8Wv397DpqpW0rwuPnluGZ84t5TxCTCPXGQ4U7juY71GrUVERr0038id9re/sZPfvLOX/1m9n+bOAFMK0vjB9TO5bm6xrhESGSb0P7GP+1+uIFMdQkRERi1rLc9sOMj5E3LITR0ZHTLCYcsbO+p5/O09vLy1FocxXDGjgE+dV8bC8mxN+xAZZhSuozZUNvPXrbV85fLJpPnc8S5HRERiYHd9B1/43Xv803Uz+cQwXyCszR/gj2sqefztveyq7yA31cPdSydyy8JSijKS4l2eiByHwnXU/csryEjSXGsRkdHs9Yp6AC6alBfnSo5vb0MHj721hz+srqS9O8icsZn8+41zuHpWEV5X4q4oKTJSKFwD71e28PLWWv7uMo1ai4iMZq9X1DEuJ5nSnOR4l3IEay1v72zgkTd38/LWWpzG8IHZRdy+qJy5YzPjXZ6InAKFa+D+l7dHRq0XlcW7FBERiZGeYJi3dzZw/dnF8S6llz8Q4k/vHeDRN/ewraaN7JTI1I9PnDuOgnRfvMsTkdOQ8OF644EWlm+p5cuXTSZdo9YiIqPWxqoWOnpCw2JKyMGWLv777b38btU+mjoDTCtK50c3zOZDc8bgc2vqh8hIlvDh+qfLK0j3ubhNo9YiIqPa2aVZvPmNi8mO46qMGyqbeWjFLp7fWI21lsumF3D7onJ1/RAZRRI6XEdGrWv40qUatRYRSQTFmfHrsvHrt/bwvWc2keJ1cfv5Zdx6fhljs4fX3G8ROXMJHa5buwLMLsnQqLWIyCjX2NHD3z/1PnctncjM4owh/d6hsOX7z27msbf2cOm0Av79xjm6eF5kFHPEu4B4On9iLk/ffQEZSTrJiYiMZm/sqOf5jdUEw3ZIv297d5DPPr6ax97aw99cUM6vPjlPwVpklEvokWsREUkMK7bXkZnsZtYQjlpXNXfxmV+vZntN24hYtEZEBofCtYiIjGrWWl6vqGPRxFycjqG5aPD9yhY+8+t36eoJ8eht53DR5Ph3KBGRoaFwLSIio1pFbTs1rd1cNCl3SL7fC5uquWfZOrJTPPz3nQuZUpg2JN9XRIYHhWsRERnVmjsDTC9K54IY97e21vLw67v55+e3MKckk//81Hzy0rwx/Z4iMvwoXIuIyKi2oDyb5754YUy/RyAU5h/+bxO/W7WPD8wq4t8+NkeLwYgkKIVrEREZtYKhMCFr8bpiF3RbugLc9du1vLGjns8vmcBXLp+CY4jmdovI8JPQrfhERGR0e3tXA3O/9xLr9zfH5PX3N3Zywy/fYuXuBn50w2y+duVUBWuRBKeRaxERGbVer6gnFLZMKkgd9Ndu7OjhI798i+5gmMc/vZDzJuQM+vcQkZFH4VpEREatFdvrmF+WRbJn8H/c/eDPW2js6OH/7l7EjDFDu+qjiAxfmhYiIiKjUktngK3VbSyaOPgt+N7cUc8f11byucUTFKxF5AgK1yIiMiodaO4CoDw3ZVBf1x8I8fdPvU9ZTjJ3XzxxUF9bREa+uIRrY8yVxphtxpgdxphvHOeYJcaYdcaYTcaY14a6RhERiRip5+zsFA9fvWIKMwd5ZPk//lrBnoZOfnD9LLXbE5FjDPmca2OME/g5cBlQCbxrjHnaWru5zzGZwC+AK621+4wx+UNdp4iIjOxzdmGGj7uWDu7I8rbqNn712i4+cnZJTKabiMjIF4+R6wXADmvtLmttD7AMuPaoY24BnrTW7gOw1tYOcY0iIhIxYs/ZB1u6qIpODRkM4bDlm09uIM3n4u8/MG3QXldERpd4hOtiYH+fx5XRbX1NBrKMMa8aY9YYYz41ZNWJiEhfI/ac/e8vbee6n785aK/321X7WLuvme98cDrZKZ5Be10RGV3i0Yqvv+769qjHLmAecAmQBLxtjHnHWrv9mBcz5g7gDoDS0tJBLlVEJOGN2HN2dWs3hRm+QXmtmlY/P3p+K4sm5nD9WUf/biEiclg8Rq4rgbF9HpcAVf0c8xdrbYe1th5YAczp78WstQ9Za+dba+fn5eXFpGARkQQ2Ys/ZNS1+CtIHJ1zf+/QmekJhfnDdLIzRCowicnzxCNfvApOMMeXGGA9wE/D0Ucf8H3ChMcZljEkGFgJbhrhOEREZwefsgy1dFA3CyPVLm2t4fmM1X7hkEmWD3NZPREafIZ8WYq0NGmPuBl4AnMAj1tpNxpjPRfc/aK3dYoz5C7ABCAMPW2s3DnWtIiKJbqSes7t6QrT6g2c8ct3eHeQf/m8jUwrSuOOi8YNUnYiMZnFZ/txa+xzw3FHbHjzq8Y+BHw9lXSIicqyReM42Bv79xjlMLzqzHtf/9uI2qlv9/OyWs3E7te6aiJzcGYdrY8wi4F5gXPT1DGCttfoVX0RE4sLndnL9WSVn9Brr9zfz67f28ImF45g3LmuQKhOR0W4wRq7/C/gSsAYIDcLriYiInJHKpk6qW/zMGZt5WiPOwVCYbz75PnlpXr565ZQYVCgio9VgfMbVYq193lpba61tOPQ1CK8rIiJyWp5Zf5AbHnybnmD4tJ7/yJu72Xywle99aAbpPvcgVycio9lgjFy/Yoz5MfAk0H1oo7V27SC8togkgEAgQGVlJX6/P96lxJzP56OkpAS3W4EtlqpbukjzuUjxnvqPuf2Nnfzkpe1cNr2AK2YUxqA6ERnNBiNcL4zezu+zzQIXD8Jri0gCqKysJC0tjbKyslHdQ9haS0NDA5WVlZSXl8e7nFGtutVP4Wl0CrHW8u0/bcRpDN/70IxR/e9RRGLjjMO1tXbpYBQiIonL7/eP+mANYIwhJyeHurq6eJcy6p3u6oxPr6/ite11fPea6YzJTIpBZSIy2p3xnGtjTIYx5ifGmNXRr38zxpxZ7yMRSTijPVgfkijvM96qW7pOeeTaHwjx/Wc3M6ckg0+dVxabwkRk1BuMaSGPABuBj0UffxJ4FPjwILy2iEjMNTQ0cMkllwBQXV2N0+nk0NLcq1atwuPxHPe5q1ev5vHHH+eBBx4YklpPlzGmjciUvWN2EWmfmj7EJcXUAzedRdopXoj46rZa6tt7+MnH5uJ06JcgETk9gxGuJ1hrP9Ln8feMMesG4XX/f3t3Hh5lee9//H1nsq9khUCAACKbImAAWTwNoFYrLVr1aGwt1LZWa12Ox9bl1Ep/tb9f28vj0S6nrV2ktaUcT61WqGKLdakbCLIvsgYIWUhCyEYmyczcvz9miAHCmsk8s3xe15UrM8/zzMP3uYZ888099/O9RURCIjc3l3Xr1gGwcOFC0tPTuf/++7v2ezwe4uN7TpclJSWUlJT0uC+cWGsznI4hlKYOzz3r1yzdUEVuWiLTR5z9a0VEjgpGK742Y8zMo08Ci8q0BeG8IiKOWbBgAffddx+zZs3igQceYNWqVUyfPp2JEycyffp0PvroIwDeeOMN5s6dC/gL81tvvZXS0lKGDx8eVqPZxpicU305HV8w1TS5Wbq+ksNHOs74Na3tHl7bWsOnLiwkXisxikgvBGPk+g7gt4F51gY4BCwIwnlFJAZ9Z+lmtlQ2BfWcYwdm8uinx53167Zv386KFStwuVw0NTXx1ltvER8fz4oVK3j44Yd5/vnnT3jNtm3beP3112lubmbUqFHccccd4dJ2bw3+aSE9zXewQNSsqrt2XwN3/XEty+6aSb/Uk0/p6e61bQdxd/qYO76wj6MTkWgXjG4h64CLjDGZgefB/a0oIuKQG264AZfLBUBjYyPz589nx44dGGPo7Ozs8TVXX301SUlJJCUlUVBQQE1NDUVFvVuGOxistTHT+6+60d8v/Wy6hSxdX0n/zCQmF0fVIL6IOOCci2tjzOettb83xtx33HYArLVP9DI2EYlB5zLC3FfS0tK6Hj/yyCPMmjWLF154gfLyckpLS3t8TVJSUtdjl8uFx+Pp6zDPmjEmGxgJdFWf1tq3nIsouKqa3CS64sg5w1HrJncnb35Uy+cvGUqcbmQUkV7qzcj10d86MXWTjIjEpsbGRgYNGgTAokWLnA2mF4wxXwbuAYqAdcAlwHtE0cJfNY1uCjKTzrhQ/vvmGjq8PuZepCkhItJ751xcW2t/Efj+neCFIyISnr75zW8yf/58nnjiCWbPjug69B5gMvC+tXaWMWY0EFV5/GxXZ1y6oZJB/VKYOLhf3wUlIjHDWNtT29OzOIExPwQew98hZDlwEXCvtfb3vQ/v7JSUlNjVq1eH+p8VkV7aunUrY8aMcTqMkOnpeo0xa6y1fd7TzxjzgbV2cqBl6lRrbbsxZp21dkJf/9vH66ucXdFwBHenl/MKTv/BakNrB5O/t4IvXTqMh66Knf+DItI7p8rZweg3dEXgJsa5QAVwPvCNIJxXRESCr8IY0w94Efi7MeYvQKWjEQVZUXbqGRXWAMs3V+PxWT49fmAfRyUisSIYrfiO9pj6FPBHa+0hLe8rIhKerLXXBh4uNMa8DmTh/9QxKrS2e1i8ch+zxxQwIj/9tMcv21DJsLw0xg2MqgUqRcRBwRi5XmqM2QaUAK8ZY/IBdxDOKyIiQWaMucQYkwFgrX0TeB2Y6GxUwVPR0Mb3Xt7K1qrTd4WtbW7nvV31fHp8IRoUEpFg6XVxba19EJgGlFhrO4FWYF5vzysiIn3iZ0BLt+etgW1RoarRv0Bw4Rn0uH5lUxU+C3Mv0pQQEQme3vS5nm2t/Ycx5rPdtnU/5M+9CUxERPqEsd3uZLfW+owxwZgiGBZqmvwfnPY/g24hS9dXcn7/dM7vr46yIhI8vUmonwD+AXy6h30WFdciIuFotzHmbj4erf4asNvBeIKqqtGNMVCQceriuqqxjQ/KG/j3y88PUWQiEivOeVqItfbRwPcv9vB1a/BCFBHpW6Wlpbz66qvHbHvyySf52te+dtLjI7jt5+3AdOAA/g5PU4HbHI0oiGqa3OSmJZEYf+pfb3/dUAVoSoiIBF+v51wbY/5voK3T0efZxpjHenteEZFQKSsrY8mSJcdsW7JkCWVlZQ5F1HestQettTdZawustf2ttTdbaw86HVewLPzMOJbdNfO0xy3dUMUFgzIZlpd22mNFRM5GMLqFXGWtPXz0ibW2AX9bPhGRiHD99dezbNky2tvbASgvL6eyspLFixdTUlLCuHHjePTRRx2OMjiMMecbY14zxmwKPB9vjPmW03EFS1K8iwGnuZlxX/0R1u8/zFz1thaRPhCMm1hcxpgka207gDEmBUgKwnlFJEbd+Iv3Ttg2d3wht0wrpq3Dy4JnVp2w//qLi7ihZDCHWju44/drjtn3P1+ddsp/Lzc3lylTprB8+XLmzZvHkiVLuPHGG3nooYfIycnB6/UyZ84cNmzYwPjx43t3cc77Jf6Fvn4BYK3dYIxZjH+l3Yj3g+XbmD4il0tH5p/0mGUb/WvmXH1hYajCEpEYEoyR69/j72/9JWPMrcDfgd8G4bwiIiHTfWrI0Skhzz33HJMmTWLixIls3ryZLVu2OBxlUKRaa4//68TjSCRB5u708rM3drF+/+FTHrdsfRUTh/RjcE5qaAITkZjS65Fra+0PjTEbgMsAA3zXWvvqaV4mInJSpxppTkl0nXJ/TlriaUeqe3LNNddw33338eGHH9LW1kZ2djaPP/44H3zwAdnZ2SxYsAC3OyrWx6ozxozA39UJY8z1QJWzIQXHmbTh21XbwpaqJr49d2yowhKRGBOs3qZbAY+1doUxJtUYk2GtbQ7SuUVE+lx6ejqlpaXceuutlJWV0dTURFpaGllZWdTU1PDKK69QWlrqdJjBcCfwNDDaGHMA2AN8ztmQgqOq0V9cF2alnPSYZeurMAauHq8pISLSN3pdXBtjvoK/jVMOMAIYBPwcmNPbc4uIhFJZWRmf/exnWbJkCaNHj2bixImMGzeO4cOHM2PGDKfDCwpr7W7gMmNMGv6pgW3AjcBeRwMLgqMj1wOyer7tx1rL0g2VTCnOOaNFZkREzkUwRq7vBKYAKwGstTuMMQVBOK+ISEhde+21dFu8kEWLFvV43BtvvBGagILIGJOJP18PAv4CrAg8vx9YD/zBueiCo6G1Azj5tJCPaprZebCF+ddcEMqwRCTGBKO4brfWdhxd+jywjK499UtERCTEngUagPeArwDfBBKBa6y16xyMK2gWzBhG2dQhJMW7ety/dH0lcQauumBAiCMTkVgSjOL6TWPMw0CKMeZy/EvpLg3CeUVEJHiGW2svBDDG/AqoA4ZE2/0xJyusrbUs21DFjPPyyEtXt1gR6TvBaMX3AFALbAS+CrwMRM2CBCIiUaLz6ANrrRfYE22F9Q+Wb+PZ98p73LfxQCN7648wVzcyikgf69XItTEmDthgrb0A/8IEIiLnxFrL0ell0az7nO4Qu8gY0xR4bPB/2tgUeGyttZlOBRYsL649wPQRedzSQyfGZRuqSHAZPjlOU0JEpG/1qri21vqMMeuNMUOstfuCFZSIxJbk5GTq6+vJzc2N6gLbWkt9fT3JyaHvVGGt7Xm+RJTw+iwHm9t77BTi81mWra/k0pH59EtNdCA6EYklwZhzXQhsNsasAlqPbrTWfiYI5xaRGFBUVERFRQW1tbVOh9LnkpOTKSoqcjqMqFPf0o7XZxnQQ6eQtfsbqGx0840rRzkQmYjEmmAU198JwjlEJIYlJCQwbNgwp8OQCHZ0AZkBPSwgs3R9FYnxcVw2pn+owxKRGHTOxbUxJhm4HTgP/82Mv7bWeoIVmIiIyJlq7fCQl55IYdaxI9den+WvG6uYPaqAjOQEh6ITkVjSm5Hr3+K/+/yfwFXAWOCeYAQlIiJyNqaPyGP1ty4/YfuqPYeobW5n7kXqEiIiodGbVnxjrbWft9b+ArgeuPRMX2iMudIY85ExZqcx5sFTHDfZGOM1xlzfizhFRKQXIjlnL91QSWqii9mjtXCwiIRGb4rr7j1Tz3g6iDHGBfyUj0e7y4wxY09y3A+AV3sRo4iI9EKk5OynVuzgP17YeMw2j9fH8k3VzBnTn9TEYNxiJCJyer3JNufaM3UKsNNauxvAGLMEmAdsOe64u4Dngcm9iFFERHonInL2yj31uDu9x2xbX9HIodYOPjlONzKKSOic88i1tdZlrc0MfGVYa+O7PT7VYgSDgP3dnlcEtnUxxgwCrgV+fq7xiYhIUEREzq5udFN4XKeQd3fWAf752CIioRKM5c/PVk8rRBy/ZNmTwAOBJXpPfTJjbjPGrDbGrI6FHrkiIiEW9jnbWkt1k5v+x/W4fmdXHWMLM8lJ08IxIhI6ThTXFcDgbs+LgMrjjikBlhhjyvHfLPnfxphrejqZtfZpa22JtbYkPz+/D8IVEYlpYZ+zm9s9HOnwHrM6Y1uHlw/3HmbGeblB+TdERM6UE3d4fACMNMYMAw4ANwE3dz/AWtu1moQxZhGwzFr7YghjFBERv7DP2S1uDxcOyqI4N61r2+q9h+jw+ph+nqaEiEhohby4ttZ6jDFfx39HuQv4jbV2szHm9sB+zbMWEQkTkZCzB/ZLYeldM4/Z9s7OeuLjDFOKcxyKSkRilSO9iay1LwMvH7etxwRtrV0QiphERKRnkZiz391Vx8Qh/UhLUgs+EQktJ+Zci4iIBM0z7+xh3k/exuP1AdB4pJONBxrVJUREHKHiWkREItqOgy3sb2gj3uX/lfbe7nqshRmaby0iDlBxLSIiEa2m8dg2fO/uqiMlwcWEwf2cC0pEYpaKaxERiWhVjW4Ksz4urt/ZWceUYTkkxutXnIiEnjKPiIhEtJpuC8hUN7rZVduq/tYi4hgV1yIiErF8PktJcTYTBmcB/ikhoCXPRcQ56lEkIiIRKy7O8ItbSrqev7OznuzUBMYWZjoYlYjEMo1ci4hIVLDW8u6uOqaNyCUuzjgdjojEKBXXIiISsZZtqGTy91awr/4Ie+paqWp0a0qIiDhKxbWIiESsqsNuapvbyUpN4J1d9YD6W4uIs1Rci4hIxKpucpOa6CIzOZ53d9YxMCuZ4txUp8MSkRim4lpERCJWdaObAZnJWOtfmXH6eXkYo/nWIuIcFdciIhKxqgM9rrdUNXH4SKf6W4uI49SKT0REItalI/PITE7gnZ3qby0i4UHFtYiIRKx7LzsfgC/8ZhUj8tO6VmoUEXGKpoWIiEhE8vosHR4fHR4fH+w5pC4hIhIWVFyLiEhE2lLZxKhHXuHXb++mrdOrKSEiEhZUXIuISESqbnJjLZTXHyHOwLThuplRRJyn4lpERCJSdZMbgG1VTVwwKIus1ASHIxIRUXEtIiIRqrqxDVecYdOBRk0JEZGwoeJaREQiUnVjO5nJ8Xgt6m8tImFDxbWIiESkWaPzOb9/BomuOEqG5jgdjogIoOJaREQi1NzxA2l2e5g0tB8piS6nwxERAVRci4hIhNp0oJEtVU3M0HxrEQkjKq5FRCTiNLs7mfvjtwGYrsVjRCSMqLgWEZGIUxNow5cUH8dFRVkORyMi8jEV1yIiEnGqG9sBGDcwk3iXfpWJSPhQRhIRkYiztboJgGmaby0iYUbFtYiIRJzV5YcAuGJcgcORiIgcS8W1iIhEHHenl7REF+MH9XM6FBGRY6i4FhGRiGKtZUtVM7PH9McY43Q4IiLHUHEtIiIRZefBFmqb27lwYKbToYiInEDFtYiIRJR3dtYBsOFAo8ORiIicSMW1iIhElH8GiusR+ekORyIiciIV1yIiEjE8Xh/v764HoDAr2eFoREROpOJaREQixqbKJlrbvQD0V3EtImFIxbWIiESMo/OtAQZkqrgWkfAT73QAEt6a3Z18/5VtDMpOYVC/FIqyUynKTiE/PYm4uLNvgeXx+qhr6SA3PZEEVxxbKps40uHhwqIskuJdfXAFIhJN3t1Vx4j8NO6eM5KhualOhyMicgIV13KClnYPO2qamTgkm9rmdl7eWEXDkc5jjvnuvHHcMq2Y/YeO8KPXdnQV3wOykmlt91BSnENeehLv767nZ2/s4mBzO7XNbupbO7AWXrnnUsYUZrJmXwOPvLiJxPg4xg/K4uLibEqG5vAv5+ep2BaRY7g7vawub+BzU4cyb8Igp8MREemRimvpYq3llU3V/J+lW+j0+njnwdkMz09n7bevoLXdw4HDbRxoaKPicBvThucAUNPk5s3ttRxsbj/mXM8smMys0QV4vJZDrR0MzEpmwuAs8jOSKchIIj8jCYB5EwZSkJHE6vJDrN7bwG/e3sMzb5ezYeEVALyysYrmdg8lQ7MZlpemBSNEYlhdSzvji7IYkJXEhorDjC/q53RIIiInMNba0P+jxlwJPAW4gF9Za79/3P7PAQ8EnrYAd1hr15/uvCUlJXb16tXBDjcmlNe18u2XNvPW9lrGFmby2LUXMGlI9hm/vt3jpeqwm+omNxnJ8QzLSyM18ez/dnN3etl5sIULBmUBMP83q3hzey0AuWmJTBqazYwRuSyYMQzw/0GggluigTFmjbW2xOk4ehJuOftff/4exsD/fHXaWb9WRCQYTpWzQz5ybYxxAT8FLgcqgA+MMS9Za7d0O2wP8AlrbYMx5irgaWBqqGONFXvrW7niybdIdMXx6KfHcsslQ4l3nd29rknxLorz0ijOS+tVLMkJrq7CGvwj4LtqW1i9t4HV5Q18uK8Br892FddXPvlPXHGGUQMyOL9/Buf3T2fcwCwGqIuASFCEY86uamo7qz/+RURCyYlpIVOAndba3QDGmCXAPKArUVtr3+12/PtAUUgjjBH76o8wJDeVoblpfPOTo/jMRQMpCLO77+PiDCP7ZzCyfwZlU4YA4PP5P22x1lI6Op9tVc28v7ueF9YeAOCmyYP5/nXj8fks3/rLJsYUZjJxcD9GD8g46z8aRCS8cra1lprGdv0BLSJhy4niehCwv9vzCk49wvEl4JU+jSjGVDW28d1lW1ix5SCv/tu/MCwvjS9fOtzpsM7Y0S4lxhgeumpM1/bGtk521DSTnuz/b13X2s7yTdUsXrkPgOSEOMYP6scds0Ywa1SBppSInJmwytmHWjvo8PrUhk9EwpYTxXVP1UyPE7+NMbPwJ+qZJz2ZMbcBtwEMGTIkGPFFLY/XxzPvlPNfK7bj9VnuuWwkA/tFzy+orJQESopzup4XZCSz5luXUdHQxtr9h1m37zBr9zdw9D6D1Xsb+PriD5k4OJsJQ/oxYXA/xhdlndNccZEoFlY5u7rJDajHtYiELyeqiApgcLfnRUDl8QcZY8YDvwKustbWn+xk1tqn8c/vo6SkJPR3Z0aITq+PG37+Huv2H2bO6AIWfmYcg3Oiv0esMYbBOakMzknlMxcNPGZfSoKLS4bnsm7/YZZvrgYgzsAr9/wLowZk8I9tNSzbUEV+ehJ56UnkZSSSm5bElGE5JCeoTaDEjLDK2cPy0vjf26cxIj/9bF8qIhISThTXHwAjjTHDgAPATcDN3Q8wxgwB/gzcYq3dHvoQo4fPZ4mLMyS44rh8bH++culwrh5f6HRYYeGCQVk8ddNEAOpb2llfcZj1+xu7RsSqG9tZufsQtS3tdHh8Xa/78JHLSU5w8cTft7N45V6mjcjj5ilDuGR4jqaZSDQKq5ydmhjP5G6fUImIhJuQF9fWWo8x5uvAq/jbOv3GWrvZGHN7YP/PgW8DucB/B4oVT7i2qApnmw408sDzG/jW1WOZNiKXO2ed53RIYSs3PYnZo/sze3T/rm03Tx3CzVOHYK2lud1DXXM7dS0d9EtJAODCQVl84vwC/r6lmqXrKxmel8bnLxnKrTOHOXUZIkEXbjn7g/JDVDW6T/gkSkQkXDgyudRa+zLw8nHbft7t8ZeBL4c6rmjR7vHy49d28rM3d5GTlkin13f6F8lJGWPITE4gMzmB4fkfb798bH8uH9ufto4LeHljFYtX7WPlnvqu4npzZSNjCzM1mi0RL5xy9nMf7OetHbUqrkUkbOnOrSizfv9hvvGn9WyvaeG6SUV8e+5YslITnA4rqqUkurju4iKuu7gId6cX8C/Kc/WP3mZ4fho3TxnCdZOKyE5LdDhSkchX3eTWzYwiEtbU9DfKfLivgWa3h2e+OJn//NeLVFiH2NEbHQdkJfP4DRfRLyWBx/66lan/7zXuXbKWA4fbHI5QJLJVN7rV41pEwppGrqPAmr2HONTayeVj+zN/WjHXX1xERrKKaiclJ7i4/uIirr+4iG3VTfxx5T7+urGaRwPF946aZvIzkuiXqtFskbNR3eRm+ohcp8MQETkpFdcRrN3j5QevfMQz7+5h3MBMLhtTQFycUWEdZkYPyOQ78y7gkblju1aIfOD5DWyubOLq8YV8buoQJg3J1txskdNobffQ7PYwICvF6VBERE5KxXWEqmtp56vPrmHN3gZuuWQoD1w1WsVZmOu+9Ppj11zI4lV7eXFtJX/+8ACjB2Rw95yRfOpCtUkUOZmUBBdvPzCLFPWZF5EwpuI6AjW0djDvJ+9Q39rOT2+epL7VEWjswEweu+ZCHrpqDC+tr2Txyn0cau0AoNndye7aVsYXZekPJpFu4uIMRdnRv/iViEQ2FdcRqF9qAtdNGsRlY/szvqif0+FIL6QlxVM2ZQhlU4bg8/kXq3txXSWPvLiJcQMz+dzUocy9qJBMTfURERGJCCquI4S1lmfeKWf6ebmMHpDJfVeMcjokCbK4OP8o9bwJA8Fa/rByHw+/sJFHX9rEzPPy+MUtJSTGq8GPRLfOzk4qKipwu91Oh9LnkpOTKSoqIiFBfzyLRBMV1xGgw+PjWy9u5LnVFXxxRjGPfnqc0yFJH8pMTuCWacV8/pKhrK9o5OWNVVQ0HOkqrP/zbx+Rk5bIJ8cNYGA/3dgl0aWiooKMjAyKi4ujelqUtZb6+noqKioYNkyruopEExXXYe5Qawe3/34Nq/Yc4u45I7l3zkinQ5IQMcYwYXA/Jgzu17XN57O8ub2WDRWNfGfpFi4qyuKTFwzg6gsLGZqb5lywIkHidrujvrAG/893bm4utbW1TociIkGm4jqMHTjcRtnT71Pd5OapmyYwb8Igp0MSh8XFGV76+kx217bw6uYalm+u5ofLP6LTY7nnspG4O718UH6I4fnpFGYmd001EYkk0V5YHxUr1ykSa1Rch7H89CTGF2Xx1MwJTByS7XQ4EkaG56dzR2k6d5SOoPJwGwmBNn9vba/ltmfXAJCcEEdxbhoj8tO557KRnN8/g5Z2Dz5rdYOkyEnU19czZ84cAKqrq3G5XOTn5wOwatUqEhNPvvDT6tWr+d3vfsePfvSjkMQqIuFJxXWYsdbyv6sruGxsf3LSEvnJzZOcDknCXPd511OH5fLHr1zC7roWdte2sqeulc2VjVh/IxJeWlfJwy9sJC89ieH5aYzIT6M4N42bpgwhKyUBa61G0ySm5ebmsm7dOgAWLlxIeno6999/f9d+j8dDfHzPvzpLSkooKSkJRZgiEsZUXIeRIx0evvfXrfxh5T7uOTySf7v8fKdDkgiTlZrAtBG5TDvJ8tAXD83mwatGs7u2hT11rfxtcw31rR3cNHkIAE+u2MGf1lQwLC+N4rxUinP9xfes0QW4NMVEYtSCBQvIyclh7dq1TJo0iRtvvJF7772XtrY2UlJSeOaZZxg1ahRvvPEGjz/+OMuWLWPhwoXs27eP3bt3s2/fPu69917uvvtupy9FREJAxXUYaOvw8vv39/KLt3ZR19LBHaUjuEc3LkofGDUgg1EDMo7Z1tjWSVaKf5rI6AEZTC7OZk/9EZaur6KxrZPURBebv/NJAH6wfBtr9jaQm5ZITloiuWmJDOyXwk1T/MV5RcMR4uPiyE5LICleq+hJ73xn6Wa2VDYF9ZxjB2aeU8el7du3s2LFClwuF01NTbz11lvEx8ezYsUKHn74YZ5//vkTXrNt2zZef/11mpubGTVqFHfccYfa7onEABXXYeBH/9jBz97Yxczz8vi3y0dy8dAcp0OSGHK0sAa46sJCruq2BHtDawfVTe6uqSJJgXaAOw62cKi1g4YjHQzLS+sqrv/9ufWs3HMIgPSkeHLSErl4aDb/deMEAP66oYo4AwOykinMSiE/I0kj4hIRbrjhBlwu/x+MjY2NzJ8/nx07dmCMobOzs8fXXH311SQlJZGUlERBQQE1NTUUFRWFMmwRcYCKawe4O738YeU+xhdlMbk4hy/OKGb26AImF6uolvCSnZZIdtrHN3Dde9n53HvZx/u9Pktrh6fr+V2zR/KZQ60caung0JEODrV2UJCR1LX/B8u3se/Qka7nrjjD3PGFPHXTRAB+/NoOEuPjyM9IIi/d/zWwXzL9Uk9+E5lEr3Dq6Z+W9nGry0ceeYRZs2bxwgsvUF5eTmlpaY+vSUr6+P++y+XC4/H0eJyIRBcV1yHk7vSyeOU+fvbmLmqb2/nyzGFMLs6hICOZgoxkp8MTOWuuOHNM55GZI/OYSd5Jj//LnTOoanRT1dhGVaOb6kY3Q3JSAf/NvL9+Zw+Hjxw7CnjT5MF8/7rx+HyWuT9+m+y0hK7COy89ianDc5g0JJtOr4/V5Q2kJblIS4onLTGe1CQXaYnxGh2XoGpsbGTQIH9r1EWLFjkbjIiEHRXXIfKnNRX8cPk2Dja3c8nwHH5cNpFLhvd805lItDo6Ej52YOYJ+4wxrH3kclraPdS1dFDX0k5dczsDsvx/eLZ7fBRlp1Db0s6H+xqoa+6grdPL3XNGMmlINg2tHZT98v0TzvvgVaO5/RMj2H/oCDf/6n2S4l24jMEV5/+6c9Z5XHnBAHbXtvAfL2zCFWeIizPExxnijOHLlw7jkuG57K1v5Q8r95GS4CI10UVKoouUBBczR+ZRmJXC4SMdlNcfYUxhhuabR7lvfvObzJ8/nyeeeILZs2c7HY6IhBkV133I3enFFWdIcMXR0NpBcV4aT9008aSdHERinTGGjOQEMpITGJZ37IqTKYkunv7CsW3OWts9BLoMkpmSwOKvTOVIu5fWDg9HOry0tnuYMsw/3SrBFcfkoTm4PV68PovXB16fj+QE/zxyn7V4fZZ2jxev9a+G6fFZOjw+wL+o0+/eK8fd6Tsmhme+OJnCrBTe332I23+/hvcemk1hlpaljwYLFy7scfu0adPYvn171/Pvfve7AJSWlnZNETn+tZs2beqLEEUkDBl7tAFuFCgpKbGrV692OgzqWtp59r29PPv+Xv7jU2O47uIivD5LnNGKXCKRzuezuD1ejnR4aevwkpueSGpiPAeb3WysaGTmyLxzGrk2xqyx1sZUk+SecvbWrVsZM2aMQxGFXqxdr0i0OFXO1sh1EO082MKv397N8x8eoMPjY87oAkYUpANozqdIlIiLM6QmxpOaeGz6LMhIZs4Y3TshIhLrVFwHibWWu/64lt21LVw3qYgvzRzGeYHCWkRERERig4rrc9Tp9fHyxioWr9zHL+eXkJmcwOM3jGdAZjK56UmnP4GIiIiIRB0V12epyd3JklX7WPROOZWNbobnp3GgoY3MwgTGDcxyOjwRERERcZCK67Owp66Vz/z4bZrbPVwyPIfvXnMBs0YVEKf51CIiIiICxDkdQCQZlpfGN64cxbK7ZrLktmnMGdNfhbWISBQpLS3l1VdfPWbbk08+yde+9rWTHh8OXapEJHyouD4Nay2/fGs3W6uaAPjCtGIuGKTpHyIi0aisrIwlS5Ycs23JkiWUlZU5FJGIRBoV16fg81ke++tWvvfyVp5bvd/pcEREpI9df/31LFu2jPb2dgDKy8uprKxk8eLFlJSUMG7cOB599FGHoxSRcKY51yfR6fXxwJ828Oe1B1gwvZhHrh7rdEgiIjHnxl+8d8K2ueMLuWVaMW0dXhY8s+qE/ddfXMQNJYM51NrBHb9fc8y+//nqtFP+e7m5uUyZMoXly5czb948lixZwo033shDDz1ETk4OXq+XOXPmsGHDBsaPH9+7ixORqKSR6x64O7189dk1/HntAe6/4nwe/fRYza0WEYkR3aeGHJ0S8txzzzFp0iQmTpzI5s2b2bJli8NRiki40sh1D+KMweuzfO/aC/jc1KFOhyMiErNONdKckug65f6ctMTTjlT35JprruG+++7jww8/pK2tjezsbB5//HE++OADsrOzWbBgAW63+6zPKyKxQSPX3dQ0uTnU2kFifBzPLJiswlpEJAalp6dTWlrKrbfeSllZGU1NTaSlpZGVlUVNTQ2vvPKK0yGKSBjTyHXAnrpWbvn1SobkpPKHL0/VNBARkRhWVlbGZz/7WZYsWcLo0aOZOHEi48aNY/jw4cyYMcPp8EQkjKm4BjYdaGTBM6vwWXjwqtEYo8JaRCSWXXvttVhru54vWrSox+PeeOON0AQkIhEj5ovr93bV85XfrSYzOZ5nvzyVEfnpTockIiIiIhEqpotrr8/y6EubGJCVzLNfmkJhVorTIYmIiIhIBIvp4toVZ/j1/MmkJ8WTnZbodDgiIiIiEuFiurgGGJyT6nQIIiLSjbU2Ju596T6nW0Sih1rxiYhI2EhOTqa+vj7qC09rLfX19SQnJzsdiogEWcyPXIuISPgoKiqioqKC2tpap0Ppc8nJyRQVFTkdhogEmSPFtTHmSuApwAX8ylr7/eP2m8D+TwFHgAXW2g9DHqiIiIQ0ZyckJDBs2LBeRiwi4pyQTwsxxriAnwJXAWOBMmPM2OMOuwoYGfi6DfhZSIMUERFAOVtE5Gw5Med6CrDTWrvbWtsBLAHmHXfMPOB31u99oJ8xpjDUgYqIiHK2iMjZcKK4HgTs7/a8IrDtbI8REZG+p5wtInIWnJhz3VN/peNvCz+TY/wHGnMb/o8hAVqMMR+dZTx5QN1ZvibSxdo163qjW7Rc71CnAziJcMvZED3v+ZnS9UY3XW9kOmnOdqK4rgAGd3teBFSewzEAWGufBp4+12CMMauttSXn+vpIFGvXrOuNbrF2vQ4Iq5wNsfee63qjm643+jgxLeQDYKQxZpgxJhG4CXjpuGNeAr5g/C4BGq21VaEOVERElLNFRM5GyEeurbUeY8zXgVfxt3X6jbV2szHm9sD+nwMv42/ptBN/W6cvhjpOERFRzhYROVuO9Lm21r6MPxl33/bzbo8tcGeIwunVx5MRKtauWdcb3WLtekMuzHI2xN57ruuNbrreKGOifYlZEREREZFQcWLOtYiIiIhIVIrp4toYc6Ux5iNjzE5jzINOx9PXjDHlxpiNxph1xpjVTscTbMaY3xhjDhpjNnXblmOM+bsxZkfge7aTMQbbSa55oTHmQOB9XmeM+ZSTMQaLMWawMeZ1Y8xWY8xmY8w9ge1R/R7Lx5Szo0+s5e1YytkQu3k7ZovrM1zSNxrNstZOiNI2OIuAK4/b9iDwmrV2JPBa4Hk0WcSJ1wzwX4H3eUJgvmw08AD/bq0dA1wC3Bn4mY3291hQzo7SnA2xl7cXETs5G2I0b8dscc2ZLekrEcRa+xZw6LjN84DfBh7/FrgmlDH1tZNcc1Sy1lZZaz8MPG4GtuJfBTCq32PpopwdhWItb8dSzobYzduxXFzH4nK9FvibMWZNYJW0WND/aL/dwPcCh+MJla8bYzYEPoKMqo/bAIwxxcBEYCWx+x7HGuXs2BGLP9NRnbMhtvJ2LBfXZ7xcbxSZYa2dhP9j1TuNMf/idEDSJ34GjAAmAFXAfzoaTZAZY9KB54F7rbVNTscjIaOcrZwdraI6Z0Ps5e1YLq7PeLneaGGtrQx8Pwi8gP9j1mhXY4wpBAh8P+hwPH3OWltjrfVaa33AL4mi99kYk4A/Qf/BWvvnwOaYe49jlHJ2FP0sn0ZM/UxHc86G2MzbsVxcn8mSvlHDGJNmjMk4+hi4Ath06ldFhZeA+YHH84G/OBhLSBxNWAHXEiXvszHGAL8Gtlprn+i2K+be4xilnB0lP8tnIKZ+pqM1Z0Ps5u2YXkQm0O7mST5e0vd7zkbUd4wxw/GPfIB/Zc7F0Xa9xpg/AqVAHlADPAq8CDwHDAH2ATdYa6PmZpKTXHMp/o8XLVAOfPXo3LZIZoyZCfwT2Aj4Apsfxj9/L2rfY/mYcnb0XW+s5e1YytkQu3k7potrEREREZFgiuVpISIiIiIiQaXiWkREREQkSFRci4iIiIgEiYprEREREZEgUXEtIiIiIhIkKq4l5hhjvMaYdd2+HgziuYuNMVHTo1RExGnK2RJp4p0OQMQBbdbaCU4HISIiZ0Q5WyKKRq5FAowx5caYHxhjVgW+zgtsH2qMec0YsyHwfUhge39jzAvGmPWBr+mBU7mMMb80xmw2xvzNGJPi2EWJiEQp5WwJVyquJRalHPcR443d9jVZa6cAP8G/EhyBx7+z1o4H/gD8KLD9R8Cb1tqLgEnA5sD2kcBPrbXjgMPAdX16NSIi0U05WyKKVmiUmGOMabHWpvewvRyYba3dbYxJAKqttbnGmDqg0FrbGdheZa3NM8bUAkXW2vZu5ygG/m6tHRl4/gCQYK19LASXJiISdZSzJdJo5FrkWPYkj092TE/auz32onsbRET6inK2hB0V1yLHurHb9/cCj98Fbgo8/hzwduDxa8AdAMYYlzEmM1RBiogIoJwtYUh/nUksSjHGrOv2fLm19mhrpyRjzEr8f3iWBbbdDfzGGPMNoBb4YmD7PcDTxpgv4R/tuAOo6uvgRURijHK2RBTNuRYJCMzfK7HW1jkdi4iInJpytoQrTQsREREREQkSjVyLiIiIiASJRq5FRERERIJExbWIiIiISJCouBYRERERCRIV1yIiIiIiQaLiWkREREQkSFRci4iIiIgEyf8H/Pcq1jrpwBgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(resampled_history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUuE5HOWZiwP"
   },
   "source": [
    "### Evaluate metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "C0fmHSgXxFdW"
   },
   "outputs": [],
   "source": [
    "train_predictions_resampled = resampled_model.predict(train_features, batch_size=BATCH_SIZE)\n",
    "test_predictions_resampled = resampled_model.predict(test_features, batch_size=BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "FO0mMOYUDWFk"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss :  0.13346794247627258\n",
      "tp :  73.0\n",
      "fp :  391.0\n",
      "tn :  56479.0\n",
      "fn :  19.0\n",
      "accuracy :  0.9928022027015686\n",
      "precision :  0.1573275923728943\n",
      "recall :  0.79347825050354\n",
      "auc :  0.9227969646453857\n",
      "prc :  0.7035706043243408\n",
      "\n",
      "Legitimate Transactions Detected (True Negatives):  56479\n",
      "Legitimate Transactions Incorrectly Detected (False Positives):  391\n",
      "Fraudulent Transactions Missed (False Negatives):  19\n",
      "Fraudulent Transactions Detected (True Positives):  73\n",
      "Total Fraudulent Transactions:  92\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "resampled_results = resampled_model.evaluate(test_features, test_labels,\n",
    "                                             batch_size=BATCH_SIZE, verbose=0)\n",
    "for name, value in zip(resampled_model.metrics_names, resampled_results):\n",
    "  print(name, ': ', value)\n",
    "print()\n",
    "\n",
    "plot_cm(test_labels, test_predictions_resampled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_xYozM1IIITq"
   },
   "source": [
    "### Plot the ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "fye_CiuYrZ1U"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f33544d0e50>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_roc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_roc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "plot_roc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\n",
    "plot_roc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vayGnv0VOe_v"
   },
   "source": [
    "### Plot the AUPRC\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "wgWXQ8aeOhCZ"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3354375a90>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XvmXWQG47eUiXYxRCCNEzSSLVTKjdRgw2qswVHCo/RP/o/vy8/SjPL8pk9T2zSIlyDAkFhcPZb8DP/we2OrDUQkWOtpWM0dEmbiDKtL9w16S7eGHDC1jtVgAqLZUU1xbz9d6vJZHykH7xYfSLD2uzzT/OHsHbKw5gU1Xa62PKKqoG4IUleymorCMjOZIrpvX1TLBCCCF6DEmkmjk5+jQGmldx0airCYvuD8ALS7TJ0TXmZtunDJqj/QAUZMLLjsrnG95raDNgFrPTZzM7fbbz0Ge7P+PR3x5FdRT0FL5xyeQ+XDK5/SE9gGcX7OalpXsB+HjNYQBOHJLosnRDZLBBhv+EEOIYJYlUM7r447hm5v81OZYSFUx2SQ0GXRuF4BMGw6VfQukh7fWSR7Vhv9emw4UfQ8Y8Z9NBMVqByqLa9ufzCP+4YeYA+sWHkV1Sw/OLMgGY8cxSl23njUzmlUt6fmkLIYQQLUki5UJRTRF1tjpig2MJNgRjt2s9R23lUQAMbOh1ovQgrHxee77gniaJVGp4qvP5Bzs+4IIhF2DSmzwVvvCA8CAD545Pw2Kzsywzn8y8CnS6pr1OFbXaUO2PW/N4+qddHbp+SnQIF07sjVEvuzQJIUR3JolUM0G1+fzfT9ewsnwvTxz/BKf3Px2bYwiuss7q/oXmPATGMFj6GJRkaT1V0VophDBjGHpFj0218dTap0iPTGdG2gzPfxjRZUa9jq9udL1y8KdtudzwwQYAXlm2r8PXHt4rknHpMV2KTwghhH9JItVMTMkWKN0KoSHcs+IeeoX1on7f3X8t3MNrl3VgCGfSNVoiBWBvmF8VZgzjxVkvcuPiGwG4afFN3Dj6Rv485s+e+hjCB2YOSeSJc0ZSXGV2+z2qqvLsz9pQ4Tmv/Ipe53pulc2uEh1q5M3LJzCxb6xH4hVCCOF5kkg1Uxo9ktOy32ZlqLbybl/ZPi6cOIovN2TTL6HtVWEthMRAWCJU5cO6tyE82XnqeOCvqXP495FFALyy+RVCjdp+db3CezEnfY5MYA5wwUY9F01K7/D7Vu4t5Lf9xYCWMLWmtNrCXV9s4eJO3KMr0mNDOWlYkvz+E0IIN0gi1UxtSBKnmxJZW1HOlxHhKCjcccoQ7jilk7WEdHrt8dcXW5y6Bhh13itcvf5JAJ5d96zz3JdnfumclC56lo+vndJmAgXwh1dWse1IOfsLqnjsh50+iqzBkttPoH9CuM/vK4QQ3Y0kUq6oKvX/zOkUbTJwjdnGzrxywkwGhiRHuH+tP7wCexa1PL7lU6guZOz/buSOlP4czTgFQmP5Zu83lJvLOefbc4gLbrrtjKIoXJRxEdeNuq6TH0wEAkVRMOjb7u15+MwRzN+aiy8LZFSbrc5SD/NeWEF4UEOpB7PZjGmli9/HbtDr4LaTBvPHib7tWRNCCF+QRMoVVcUxLQrFUbrxYHEV57zyK0OSIlhwawcmhg+Ypf00Z66EDe9hBK7I3Q/69TDhTxQnjOOHI8sA1+URXtz4IslhyU2ODYkZwpBYqb7dk4zvE8P4Pr6diG622lm6q4C88lpqLXZqLXXNGtS5fqMbVuwplERKCNEjSSLlksotJWVcNe9N4lO1IptWm9Y3sPtoBdVmK6GmLv7SnfFvmPV/8NPdsO0LyF4D2Wt4InEod1zVsl7RjqId3LT4JgDuW3lfk3MhhhBWXbgKo75lsUgh3GUy6Fh+54mU1rScPP/rr78ybdo0F+9q213/28LS3QVM7u96U28hhOjuJJFypd8JJKaMJjFmIARFAZAQEeQ8vS6rhBmDE7p2D0WB8EQ4/nYwhkJVIWTOR8nfSfw7pzdtO/hkps6+n2tHXkteVZ7zsFW1Mv/AfGqsNZz73bnoFX2TtyWFJvHsCc8SbpK5LsI9JoOOxIjgFsejg1wfb4vdrrLuYIn2/hBJ8oUQPZMkUq6c+iToDCzJXc3t35/NrN6z+OfMf5IeG8qh4mo8upgpaTic9RJUF8Pzw8FSDQXNJhcX7MTY5zhuCc+A8AznYZtqZ/2RX8k3l3GgrOUmvHtL9/LWL/cxLlLb6gaDERKHER+ayLC4YR78EEK0pCgNRUs//P0g4UFd++smMsTAuPQYWU0ohAgokki5EqRNJrfarVjtVuyqNmMqOSqYQ8XVbW8V01mhsXDrdqho6HGithT+c6r2/KPzW7xFD3ynKBwxtPwa/5oUz2GjkbdzlvB2zpIW598/9X3GJo71UPBCtKQoChnJEezKq+C3/cXOkg9d8fwfR3P22DQPRCeEEJ4hiZQrmQugIg97iDacV79yr37JensrrjotNFb7aez42yF3S+tvAVwVSbjZXs53annDAUsVWGrYFBxEpU7H5fMvZ2LyxCbvCdYH87fxf2NwzODOfwYhGrll9iA+X3e4y6sPl+0uAODWTzfz6drDzuOqCkFGPX+bM0iqxAsh/EISKVd+fRGyVmCfdTsAPx/8ma/3fs2gtFBiQhOJCTWxJbuUg0XVzB6a2PWJ522Z/UCn3jbP8eO06t+w8AFuTkrgF0ex0bV5a1u8z15Xzs2T7gJFQafoGBIzBL1O36KdEO6YNzKFeSNTunydP7y8ik2HSwFc9mylRodIIiWE8AtJpFwxVwJg3LtYGz8D7l91P7ePv50rzzgZgDs+38z/1mdz65zB/HVONyicOfUvkD6NZ399ga0HFrToIfhvZATLwkJZVbiZVT9e7Dx+9sCzeWT6I76NVYhmPrhmMluyS1scv/rdddRYbHy85hBPnDPS94EJIY55kki5MnAO5GxkuimeqzLOctZzGhA9wNlkz9EKACpqLX4JscN0Oug9keDpf2NiVSFYa5qc1isWys0l1Kna5OCS4AhyVDNf7f2Kyv1NyzGM14dzicHFqsWIFJh1PwTJKkHhWeFBBqYNiG9x/OLJ6by9UltoceOH6zt1bQWF8yakceKQxC7FKIQ4Nkki5UqqtjFxqKLntgm3uWxy8vBkNmeXYTR4YeK5N6WNhz/Nb3F4PPDewge0IUBgQWg5dyRpydJCe2mTtgvtpRy/fw06R7+WQYVkm2NT5oQMGHBi12IMjZdkTLhl+sA4ZyL149a8dlq3buOhEj69fqqnwvKoxMggggwyvC5EoJJEyhXVMfClNCRJBdUFPLvuWZLCkrht/G3UWbWVfCZ9N0uk2jLzHug9BWx1nKTaebPiAGWNeq6KrVU8fugHAE7r3avJW2+ssvLn/Bz4/m9djyMoCm7d2vXriB7vxCGJfHztFIqrWhYRdceLS/awK6+CnLJajn+6ZSHcQNA3LpSld8yUsg9CBChJpFxxlDtonEhVmCv48cCP9I3sy63jbsVicyRSjXqkVLXttUkB/xehMQQytCnqOmBKs9M2u40VS8rZV7rP+VnKzeVUmCt4JcxAYa90UAEF+tjgsjro0CdWgbJDUFcGX17PoEoVKr9x//2GEJh8PcT06chdRTemKApTB3S+anp8uIn/+3obVXVWdLrA+vOZXaL9JyarqJp7v9pGfXg5OXUsLOnYfzQGJ0Vw+dQ+gf93kBDdkCRSrsT2h6k3a8NUDha7NhcqqzyLKR9NYVLoHUAoaw4Uc5NjJOutFQf4x487XVxQc8nkdP5xdvedEKvX6XllzitNjv13x395eu3TAHwW1LT9lAu/IDU81flaQSHUGNr6DWxWeCJNm7+VOZ9UgJwOBmm3NF3pqDNoCaIQLkzuH8fC207wdxgurTlQzAWvrwbg4zWHmp48fMjFO9o2KyORmDBTp2Ix6hUZXhSiFZJIuZI0DE75R5NDvSN60zeyL1nlWVRbqwmNyCbEOJRzxqW2cpGm7p2Xwbnjel4hwfMGn0d0UDTVlmoA7Nh5/PfHATj323NbtL91/K38acSfXF9Mb4Arf4DcjQBkZu5h8GA3V0T+oJWqYM0b2k89nQHOfh1GnufedYQIEBP6xPDm5RPIK69tcnxPZiaDBrtX6+1wcTVvLN8P0KWhy1CTng+vmcxYKTEhRAuSSLkp1BjKd2d/xzNrn+H9He8zNDmGpx6d26TNtTP6c+2M/n6K0D9CDCGcMeCMJsd+y/mN1bmrURoN7JntZqx2K8+vf54qS5XzuEExcPqA0+kd0Vs7kDZe+wFyqpYxeOJM9wIxBMOC+8BaB/V1r8yVYLfCF1dDwa5Of8aWFBh2FiSP8OA1hWhKp1M4aVhSi+PLag8wc4p7w9eVdVZW7ilkb34lxk4WEq4y26g22/gls0ASKSFckETKlZpSWP8fCI6CCU17T6x2rTyAUS+bsLbm37P+3eLYu9ve5Z/r/wnAG1veaHLucMVhHj/+8a7ddOyl2k9jX14PWz7Rni9/pmvXb+7Qarjye89eUwgPCw8y8ONfj+/SNfrerS0wWbjjKH+bI7seCNGcJFKu1JTAoocguk+LROqv4/7KNSOvIcwY5vbl1hwo5tkFu5k6II5bTzo2/yI6e9DZ6HX6Jr1Rv+X+xvqj6/lu/3cY9UYenvawZ2866z5IGAx2m+euufYtqDwKWSvgURe1tJqzmUEfpG31M/Muz8UhhI/MHZ7MT9vz2J5TTsb987nn1KFcMa2vv8MSImBIIuVK/aq90oNar0b8QDj+DlC0ydJtTph2Ia+8ljVZxazJKuZwcbXzeESwgYfPahgeeujb7ZTXuC7wefLwJOaO0Lba2HakjHcctXNceeCMYUSHapNK31qxnyCjnsvcHArwlqigKC4bdlmTYxmxGaw/qhVR/HLPl032+EsJS6HLG7RFp2sJjCelTYRPLgZLtZYkucNWB8se13o4u8oQBMPPhpDorl9LCDecPS6VJbvzMVvt1FrsPPjtduyqSniQgdNH9SLEJJPQxbFNEilXgqNB0YNqaxgaGna2llB1Qlijv2i+3HjE+TwhIqhJIjV/Wy5Hy+tcXiMtNtSZSB0tr21ynebunKutNlRVlad/2o3VbueCCWkBt+pmZu+ZvHnym1z787UAPLnmySbn70oJwB6cASfC3Ye13xvuWPUCLH1Me/6Thz5P6SGY86BnriVEO04Znsz2h0/h4e+288Fv2mrBh7/bAUCd1c6lfv5PmhD+JomUK2FxcPVCKMzUhvgq87RJy8D729/no10fcenQS7l02KVtX8dh5pBE/nPlxBZFA4ONTRObB04fTq3F9T/QGSkRzufDekXyz/NHt3q/qBBt/lZ5rRWzzU6IUR+whUPHJY7jb+P+Rn51vvPYp7s/xabaeCr3KZYsWMJrc17DpO/csm2v0Btw+4/OuMu13itzVftt27Pmde1x5XOw9X/a0OXoC7t+XSHaYdTruO74AYSaDJitdt79NQuA//t6Gy8t2evyPSoqqgqnj+rFA2cM82G0QviWJFKtqV89VrQXKnKdQymldaUcqTzSZK5Pe/Q6hRMz2t/H67RRKW5dLyUqhHPHt19KIa9MWzbdKzo4YAvxmfQmrh55dZNj2wq3saVwCwBr89byzrZ3Glb1OfQK78WYhDEB+7mcIpI813tkDHZu4UPZIfj6z1rPaXv6HQ8RyZ6JQRyz0uNCuXfeUACKqsx8t1kr8ta8PENz76w6wOjeTYe1gww6Zg5JbPGfSSG6I0mk2jP7/iYvu9OqvdwyrTJySlT3Kkj53qnvkVeVx7wv56Gi8vKml122+/qsr5tsJN3jnfQITLkRvr8Ndv+gzeX78pr239drHFwXmNufiO7phQvHcO+8jDbb/LQtzzkE+NdPNrU4f/OJA7njlCHeCE8In5JEyh3Z68FcAYClRJvkvf7Az4yzKozqMwtdgG5JkuvokSqp7tw+ZP5i0BlIi0jj3JhzqYpu1vOnwvwsbdPly+ZfRq+wXi6u4BsqKr0jevPE8U8QYvBRshqRrA3phURrNbPasu1/2mPOBnj1uA7cRIXek+H05zobpejhFEVp9z9oZ49N5WBRdYspDd86erJeWrqXxbvym5xTVZXYMBNPnTuK3rEdW9QjhL9IIuWO7/8GedpQkxobA1ERLC/ezvLi7dyx7GGuuGGzZ1ZkeVj9X2Dbc8pZtjufmUPaH14MJCdEnsDMGTObHFNVle1F2zlUcYgKcwW7zbv9E5xDZkkmn+/+nIHRnVuI0FjviN70juzdfsOk4fCHV9pvN/QM+PwK7fnRDm4CfXQbDJ6rrRJMn9qx9woBRIeaeOjM4S2Ox4aZnHOsduaWu3zvC4v3cOaYXkzoEyurAkXAk0TKHanjnHOkzqKOw5SyCzNziWD0nHu1OkEBaO6IZHblVXDbSYNJiAjMGDtKURQ+P+NzDlV0fK8xT6qz1XHpj9pig2fWeabYp07RsfSCpcQGx3rkegz/A6RsgroK999z8NeG1YUfna89jrscIltu9yNEZzxw+jAumpSO1W5vce60F1YC8Pn6bD5fn82coYm8dcVEX4coRIdIIuWOMxoqdQ8FXM/YCTwDEsJ58aKx/g7D40KNoWTEtj0/wxeuHXkt2wq3dfk61dZqNhdsxq7auXHRjYSbwt1+72n9TuPsQWe33iC2X8eCiekDeVuh/Ajsd8yr2vA+o6M3wcH67UFUSBwOc5+AQJ/sLwKOTqcwJDnC5bknzxnJD1tzWbGnEIBFO/O55K3fmrQZkhTJ/acPDfyFJuKYIYmUEJ10y7hbPHKdCnMFMz6ZgVW1sr1oe4fem1WW1XYi1VHBUfAHx38Vfr4ffn0BgJjSLVDaqN2B5TBoDoTGQ0xfKRAqPOLCSelcOCmdT9ce4q4vtOHoVXuLmrRZtbeIEzMSiAltWRLFoFcYkhQhSZbwKUmkOmn90fVk/vIPxhljGHLm647aQoGlzmrj1H+voHdMKO9eNVH+cglQEaYIfjjnBw6WH3T7PZ9nfs7Cgws5Wn2UOlsdQd4YXp79oDZPymZm8+bNjB49Wtv8+ae7tfMfOIb7QuPgjj0Nm0UL0UUXTOjN4KQIquoa6urllNZw5xfaXNXL3l7T6ntlNaDwtcD717+b+DnrZz6q2QM1kPjemCbn/hE1hinnfgDAu+v+xX+3vAVApApPV9gY1Ljm5rlvQt+OrKhy39GyOvYXVLG/oIqpTyxpcu6Z80dx/CA39ooTPtErvBe9wt1fgWjSm1h4cCEAd/xyB8H6YG+FBkC+LZ+kIzmc1+90Jg09E0oOQMkhqCuD6iL4/ErQ+fivk5BomHEnRLpXf010H4qiMDY9pskxq83Oir2F7MuvdDmivD1Hm7j+0tK9ZBVVER8exK1zBhMVGvilakT3JolUJw1ptC9cvr7pn2pzXcNKlEpLpfN8PrDGXs6gisqGxu0tYe+CmDAj0aFGSqstLYrm1VlaTvQU3UdiSMMKzGWHl/nmpgdgT8keXjnNsWIwbyt87KisnvmD128fbrcToTbbgDGyF8z4u9fvLfzPoNe1Oefz8R938sby/QB8vyUXgD5xoZw8vKEYbXSIkbAg+WdPeJb8juqkcwafy4mpM6iraLnnXXRYkvP5FWNu4rx+p/P01tdZmLOS0DmPQZ+TGxqHxnktxohgI7/dM9tlHSlX8wtE99E7sjcfzfuI7Mpsn9zvyVVPUmwrZm/pXk7+otHv3/RUn9wfIEgx8OGQaxgSmgRfOKrhL3kMdEY47m8+i0MEpttOGsy49BjMNju3fLwR0PYErC8KChAeZGDJ7SeQGOndHlxxbJFEqgtiwhIgrO3hsYiQGCJCYrDt0BKX8Kg0iGr0j8//rga9EU77J5jCPB5jsFHfonDeztxy1hwoZmRqFP0T3F8hJgLLyISRjEwY6ZN7Hdh1gB9rf6TaUo3eD3Oh8qryqFOt3J//C2MSx8DwEyBXmy/Dhn/BkYVN36DTQ+LQgKzv5kpscCyXDbuMMKPn/w44VgQb9cwdofU+7cgp55tN2n9yFUBFK1BcWWfly41HuOGEY2hHBOF1kkj5SJVVq9AdZmj0F6XdDtu+AFQ480WfxTJ/Wx4vLN7DX2YN5PaTZVKmaN+wkGHceOqNfrm3XbUz6cNJ1Nnq2Fm8k53FO7UTUY2W0Ne6qCuWdcA3AXpIn9AUTkyb0W47q7WS2pqSLt/PpDehUzq5mbkhOKBLX9x9agZ3n9pQIsVmVxlw748AvLF8vyRSwqMkkfKRk/uczKDoQaRFpPHL4V/YXLAZrLUQE6k1+OkGSB5FbEgclw671Pm+lza+hF1tOp8p2BDMOYPOIT4kvlOxZJdUA1qNlptnDSTIIKutRODSKTreOeUdthY2q85eVwF527Q9Bxvb+Y3vgvOAJ+O0Aqx3/vp/7r/ps67fd3hdHR/nHKVT6dDI8+Hct7oehI/odQp3nDyYZ3/OlK1nhMdJIuUjFwy5wPn8w50f8tGuj7QX0Y6hh8I1ULiGgdEDmyRS72x7B4vd0uJ6FruFm8bc1KlYai3assGdueX8vr+YGYNl9Z4IbKMSRjEqYZR7jaOGwdJ/gKVa6zkJcGtC6lgSGkSQXW2/sQfU6bTUaXtQEC/HxqHH9X2Hm63MqG02v9LqWLSy9XOIGwgoMOxMbRi1mzBb7fxrUSahJj3nj+9NTJjMFxVdI4mUH8xIm0FciGOSeeEe2PyJ9nzSdcQkNK3YfeOYG5v0SK08spKN+Rsx6jq/pPeeU4fy49Y8AMKCpDdK9DDTbtZ+uol/t9+kiWXLljFz5sxO329d3jquWnAVAK9HtT4nS6/oWXvp2qZ/12z8EL5xDPEue0J7zFoBV37f6Xh85Wi5tkJ6Z265c48/i03lphO7vk+mOLZJIuUH01OnMz11esOBjd9BRS6MuAqi0pq0vWbkNU1ez0mfQ2ZJZpc2ye0dG0psmIniKjO9Y6SbW4hjyeiE0Tw49UGOVh91eX5n0U5+yf4Fm2pj+sfTm55UVejfH1DBZtUe1QPwnxEurqSCotc2vu7kfCqL3UKoIZT7p9zP3H5zO3WNejeeOICkyCDMNpUXFu8B4JkFu3lpyV5nG5tdRa9TuHxaH+45tfv0sgn/kkQqEJz2HFQVaNtttKN/dH/6R/fv0u3qrDZCjHpCTXriw3vGZsZCCPcY9UbOG3xeq+dzK3PZWriV4tpiaqw1rV9Ip0CbM6wc6+VstW20aV+5uZx7V9xFyW5Hr1dYAiQMbvtNzQTrgzm578ncPGsQoIX+r0VaMlVjsTVtbIPXf9lPanRI88s0kXnQwqHVWfSNC5PpEcc4SaQCQcY8qCnVNoptTVzDKpNaay3BXZj7EWTQs+ruWZitdg4WV7faLiEiiHBH8brSajMl1S3naoH2l1KfuIYhgsPF1Vhbme8RFWIk1jEnodpsdXa3u9L4GvnltVSZbS7bhRj1JEdpvx42u8ohL3wmIY4VKeEpLD5/MWZby/pzLdjMYLe2PF6YCf+Z1+VY/i8hjoVhoVhUO48f/aXhxP6OX6ugpoDrRl0HwN/mDOaGEwZgb1bg9euNOdz7lbao4YFv3Nj3cqfWZtXds9pNvETPJYlUoNj6Ofx4h+tzih4eLAa0VXwb8jfwxHFPkNSo8GdnVJutnPjsslbPv3LJOOaN1Lbf+PD3QzyzYLfLdlEhRjY/2FCk8aI3fyO7xPX/ZP88cwB3zdXmga3LKuHyd1rfM+uZGQ1/MT347Xbmb8tz2e74QfH89+rJAFTUWrzymYQ4lhh0BgzubPljbGVqQGo0nPSYtpVQF9xorSSpNhsLKtgsULTHEWCw21sSfRqs9Zq9uPFF/rfuBVB0WiFkfct5pnZVJXmEFbtdxaCEMC7kBmIMrkslHM4+wrJsLYmc+6/lRAYbCTHpefKckUzoG9uJTyu6K0mkAkVwFMS2MmSnOCaE5+/i9S2vAw11qbpCURT6xrU+RyrU1DARPSrE2GrbiOCmfyGlxYRg0Lnu8o9ptO9ViEnf5v11SsP/FhMiglptmxjR0Dvnyc9ktdkx6DtZZ0eIY5ne4JEJ/wOBu+pfWOvg+eHaNIgOCI2J5j/RWpmZXL1juLG2sP03qqCLXcwjJ57pcsP3JUsLOFBj4mBRNRW1VipqtaTqrRUHyK+o4/hB8S3+bhQ9k6I237sqwE2YMEFdt26d167f1RUxXmO3waPxTE7vRbVOx6qLVhFpivR3VF7l7+/itk83sTm7lEfPGsG0gZ2r2dVT+Pu7EA2O6e/CUtPhREpVVY7WFmmrnz+5GKpbSaLGXg4n3AnAnxf9mf1l2vjhO6e8w8TkiS2aL1u2jKnHHU9BhTY94YFvtrNkV77z/DljU3nuj2M6FKvoHF/8mVAUZb2qqhNcnZMeqe6iuohqVKp1Okw6ExHGiPbfI7rk9wPFHCmtIVr2JRQiMBhDIDq9Q29RgGT6aC9Of9FRbqZRB8L2r7THtf+B7A0A3KezcLXjj/2fFvyJofamZWJOt5k4u9RK0O5w6tdZv2S2URRlptZi46glmH/svJ7TX6xo8j6donDN8f05c3SvDn0GEdgkkeouKvMpcgwzxYXEuexqFp6TU1rDkdIaIoINDEmWpFWIHmHwKdpPYwlDYdnj2vPczQBMAkamJLE1WFvVvFPXdKHLTl0Ng61HodFOPQZgTF0dBmCQHs6zzmdBzgTWq4Ox0ZCI/W99tiRSPYwkUt1FVT5Feu0PY25VLju+v4lhp78MwIeb32D+htdcvi0FPc/M+jf0mQbA378+n78XFpGIi0KcITFwSaO9Jz66EKqLXMcz9hIYf6X2/NBv8PP9rcd+0ccQ5hga+/l+rb0rvSfBKf/QnlcXM3bDnbC3lU1nT3rY+ZnY8F/Y8L7rdp38TPvWL+IL08PoVIU9jz9IWJDeWXPLale5xnwb5Tottssq32aouWGH+V4xwUQGaXMjMk3DuLtSq2ofbi/n3tKHne30OoVBiQ2bRj9jv4jVVm3vw1k1PzOnZoHzXFy4iQRHqYoyJZyr6hoWJtxT+jAR9nLn6wEJYRgdSfdPxtm8UXU8ABnm7Vxe+Y6zXUc+U4bJ7vwuAukzzY3M4trad7VF+AmD4YwXtA2LhXDXCXfC0DPA1nQF8Ts2M/uqmq6kXpK/njcOfAvAtSktF/tcln4Kd+76DY5u5WrDfK42zKdgxLXkTbmfz9Yd5r+/HaRfG3M4RfckiVR3ERJLpN2OoqqoikJVo9UwOZW5bNa5XsZfZq7WSisAX+75ksVlmcwsyee0KhclAsISm77O3aQVCnWl/8yG57VlkN366jsaL6Mu3NN625DoRu+xEFW+G8pdN63/TACU57R+zU5+pmBrJeN1jhVCVsePY36/AdhZW8RRx/DAn40HyNDvbLhOoykcplgjG3K0WBMoJSO4UTuA7IanhcbZbKjQ/nI+Tp9NhrFR21LHDxASHM+G0lLnqd5BmSQrjf5r3Ojj2WKGsCFXaxulyyfD1OiaHflMVqA6ED/TAa4zOb777DUw6wGI6NpqVnGMURRIGtbicDAwvNmxlL4zOYyF4ppiSkpLiImOAeD3vN8B+O+hBcwbfz0jdsZC8X4oO0zCtjdJmHQ+79Rp/2lYvqewxWplnQKXTO7DScPk9253JJPNmwnoiZzFBzhSsIMCcykDYjOISB0PwJHSLAoOr3L5FpPOyLB+cyA0lsvnX87G/I28M+YOJsZktGysN4DjmtqFN2hLjl2J7AXRvbXnNSVQkNl63L3GaNWNAQp2N02CGguOgkRHXFYzG354m3Fjx7luGz8IQh1LjMuyoayVGlxd+EyHMjdRXaetxAkLMjTqvbGzxd4XVa99puDSvejryho+bnQIkY7VOgXWIA7ptTkdis1MaKONd3U6hcGJDcOGO8zJ1DjmvhkrczBVNWQPsWEm5+rESgvsNgxyngst3ILS6DP1SwgjyNF7mWWJpsigFQvU15URXNpQxbkjn6no0F6mTxoXMJ/p+4M6nv05k8nJOj6dtA9+vk/b++3mdZ2uot1dBPTfUceQxt/DW1vf4t8btM1+Lhl6CXdPuhtWPAeLHb216VO5PexJvtjYeq3AUWlRPHWu6/0kDTqFgYnhMqWjFTLZXLgvth+psf1IbXY4NbovqdF92337kUrtD3FK/9kQkdZOayC1lSSmuZAYSJ/sXtuEIe61M5gojxrq3nWj0lpsrdOqDnym9NEnug4NaHKVPpNavUyC48epf3KrbZv+nzgWcLXtBoQD4xsf6DOz1Wv2dfw0XLOfy3btfaZl1UbndxEIn+nI5i0ATB0xAMqXayeGzOvxSZQITFcNv4ov93zJ4YrDpEc4JsNP/yvs/hGy18Kh1Tx13Pf84errsTUrVrxsdwHv/prFluwyTv33ilbv8afp/XjgjJY9Z8L/JJE6RlhsFgqqC9Apui4X8hTCn+x2lUU7tWXmJw1NhM9/0E5knObHqMSxTK/TE27U5gYuyFrAOYPO0XafOOlR+I+2R6DBXM7xg1puJdM3LoydueWU1bjuKS+qMlNQUUdJtRuV5oVfeDWRUhRlLtrm5nrgLVVVn2x2Pgr4AEh3xPKsqqr/8WZMx6q8qjxUVFRVJbsim35RWs9EZkkmm/I3NWnbK7wX03tNl25kEZDqrHYumpTO1uxSwu3llFl0BJli+DInkf5r/onSaLrChD4x6B3FYVebB7Jfry2Dj6g8QFLxOipDUsmLn0rf+HCmO2qFldVY+H5LTqv3nzM0iaRIbUhy/cESduW5nsgXEWxssjrr07WHsNpVEsKDmD00yRmX6BlqHXsKbsjfwIajG5iWOg36TIWRF8DWzyAyBcodQ9thCdq0A6BvfBifXj+11es+/uNO3li+n4SIII6W1xIbZnIuvBCBwWuJlKIoeuBl4CS06adrFUX5VlXVHY2a3QTsUFX1DEVREoDdiqJ8qKqqpN4eVm5p+Mt+S8EWZyK1Nm8tT655skX7r878ioExA30WnxDuCjHpue0kbdPabzYd4a9F/yCWcoq/3snuoMcJUhrt/dZou7RdkTfwcP4MAM7VLeefJm2l60l1TzN01CRnIlVYWcd9X21r9f7948OdidT8rbm8tdL1Nij948OaJFIPf7eDasd+kf+9epLL3gnRfb025zVO+UIrrXD9outZeN5CksOSoWCX1mDRQ9oPQPJIuGGlW9c9WKStCHlj+X7eWL6f/glhLL7tBPmPbgDxZo/UJGCvqqr7ARRF+QQ4C2icSKlAhKL9jggHitHWBwkPGxIzhFvG3kJOVQ59Ivs4jw+MHthkJ/hv936L2W7GpJcilCLw9YkL46JJDQUaNxw5A53aUPNnfJ8Y53ZFibaxXNRXa5tR3g+ytDbHjR3BkL5xzvdEBBuaXLO5xMgg5/Ox6TFcNMn1ZtoJ4U3/DJ0zLpUPfjsEIBvc9kC9wntx4ZAL+WT3JwC8vOllHpz6IIahZ0Klo+K53aKVXynOAlV1a07f7KFJbMkuw2KzU1hpZn9BFQ9+u90jPZoZyRH8cWLHCpyKlryZSKUChxu9zgaazxx+CfgWyAEigD+qqmr3YkzHLIPOwLWjrm1xfHLKZCanaF9Lna2OLzK/QK/oSQlP8XWIQnTYmN7RjOkd3ejIu622Pc3xA8CmbVoi1X8mD14wvUm7xIhgnjhnpFv3P21UCqeNcu/PyryRKXzw2yEGJITRPyG8/TeIbue+KfexIGsBJXUlfL33ay7OuJihJ/wdTvi71mD/Mnj/LK3cgps9ShdM6M0FE3pTXGVm3KMLAXh/9UGPxTxnaBJx4UHtNxSt8lr5A0VRzgdOUVX1Gsfry4BJqqr+pVGb84DpwG3AAGAhMFpV1fJm17oOuA4gKSlp/CeffOKVmAEqKysJDz82/5KzqTYOmQ9RZi1jTNgYf4dzTH8XgaanfRfDtz1BQuFvZA66jpxU30xS/3BnHQsPWpnXz8gFQzrf49vTvovuqrXv4VDdIZ7JewYAAwYeTXuUcL3WLu3wNwzc9w5Hes1lz+A/d/ie2wqtHKns+r/Zqgqf7NZm0LxxUigmffceJvTFn4kTTzzRL+UPsoHejV6nofU8NXYV8KSqZXN7FUU5AGQATaqVqar6BvAGaHWkvFkv4liv0aKqKmZ761PU9Ioeg077bWO1W7Gproc1AIL0Df/LMdvMqLj+C0Cn6DDqtLpLdtWOxa6tXlm+fDlTj286CdOoM6JTtImWFrtF24jUBQXFOTwZSJ/Jle7wmZYvX870GdPb/EyN7xnQzNWwUtsKZPDpf2NwZC+w1rXeXm9sqJZus4K9jdkHxuCG55Za51MVlUd++4UgzFwzZxwje8diUxy/VnabNuTjoCg4a2apqNSpxobeC5uZlSuWM2WytomuQadg0Gm/d6wqWBWj44Zqi0rdwYaGiu91qg5Vcby2N/1Mep2C0XFNu6piVholfdaGz4Q+CJNBj84xxGSx2Vss7W/ymQw9q+J8W/9WrFuyjqWHl2LFyuBxgxkco83p42ttl4XUsSeROtH1e9vS8Xe4ll9eyyePL8ak11EUMYBzx6VhMnTfCez+/nfbm3/rrQUGKYrSDzgCXAhc3KzNIWA2sEJRlCRgCLDfizGJdljtViZ84DLpBuCRaY9w9qCzAfhs92c8seYJl+0MOgMbL9vofH3hDxeyp2SPy7YXDrmQ+6bcB2gT4S+bf1nDyQ+atv3fGf9jSKxWi+rhXx/mm33fuLzmqIRRfDjvw8D8TM10m8+0pu3PFGmK5D9z/9Pwj0agOvQrxPZ31B9LhaoieKZ/6+0v+C8MO1N7vup5WPKY63YhsXBXo4nnL02AMm12gwIsAa1c9n9hUfxlXJN9KgAn6Dbznukpl5dUgNm1/+aIo3LXa8bnmatfCy7q7x6Nm8b0IzcDEEM5G4NvaPUjPR16D28Xa8OXN+m/5u/Gz1y2swfFkFH2svP1yqBbSFMKAVhtG0b0jQsYmhIJwD1fbuV/67NdXmdsejRf3Tjd5bme6PHjHmfqx1Mx6oz0i2xUu+2oY/VDYvOa6b5VY9H+Y2W22bnny60kRwVz4pDEdt4lWuO1REpVVauiKDcDC9DKH7yjqup2RVFucJx/DXgUeFdRlK1of2fcpapqobdiEu4x6VofdtA32sdMr+hbbdu8Z8KoM7rVVlEUZzu73Y5O1/R/SY1Xqhh0hlavWd9zUi9QPpMr3eEz2e32Nj+T2W6m3FxOYU1h4CdSUemQNh7mNkpe9G3MEVEa/R5U9K23NTQ7rje13land/YA6BSd1utUfwsFTI7l7SpgNOgw4eghUvRN2hr0CnrH7x9VZ3Be00TTawIENepx0Okb7q80u6Zepzgn6Nv1piY9FRYMWFQ9RsXGEN1h8hv1Xhp0Squ9Gsfacv3MEm2nh4HRAzHqHb+2dhvkO7ZI+uIauGFFw+4MPpYeG8qz54/m6Z92kV9RR9Ax9v14mmwR04y/uwhFA/kuAkdb30VOZQ6nfHEKoYZQVly4QlZ8epnf/1z89hr8dBeMOA/Oe9utt6iq2uOW67f1PXy480OeXPMkZw88m0emP6IdVFV480TIcfQAz/g7zLzHr5tsH/fUErJLarhl1kASI4PpGxfGcYPi/RZPZ8kWMUKIbk1F5fzB56NTdJJEHQv2LtIeB53kVvOiyjpOen45Mwcn8M8LRve4hMqV3cW7AZxD9oDW1XjNEngsUZsTt/wZ6DdD+/GTSsdeoi8sadiDc9Xds6Q8RwdJf54QoktSw1N5YOoD/N+U//N3KMLbLDWQ5SgkOWCWW29Zta+I4iozRytqj4kkCmBXsVaE840tb3DGV2fw04GftBM6XdNevPfOgELXcxJ94YmzR3Lx5HQunpyO0bFyTwrud5z0SAkhuuzh1Q9TYa5wee6kPidxSl+t4vP2ou38Z1vru0DdP+V+ooKiAHh769vsLN7pst3Q2KFcPfJqAMrqynj0t0dbveZVI65ieJw2uXdB1gIWHlzosl2kKZIHpj4Q8J8pvyCfhKIE/3wmFZh4BvfHTCCqMBPm38nbljx22qtbtk2bwNC44ezeM4HbDJ/RPz+Pq18vAyAsqGE+16iQMfxedyUAMZajnF3wmvMSOkVhbHq08/Uryh/Zbtb2Cp1SNp9hVQ0LvBMjgugdGwpAqTGR+6ovdJ67MvexJoVah6VEEmLShtQWBc3hq0ptM+CB1ZuYUdqw2CM8yMCQ5AgAbCr8zXIjdsdqy1kH32P9uuedbfvEhxM/9TIYMhedXfu+i2uLKa4t5ukV/+O71dpkbqMunX8NmA37FgOw7aN7eD3uLuxKyyG+mYMTOH+Ctvh9X0Elzy3MbHJeryhcMjmdyf3jWrzXHaeOTOHUkSlYbHY+XqMVi92VV0FiRLBsYdQBkkgJIbps2eFlFNa4XifSP6phRVxBdQELsha0ep27Jt7lfL4hfwPLs5e7bFfbaBl+na2uzWue0f8McPw7s690X6ttE0ISmiQdgfyZCqsL/fuZjn8cMhfC9q/YkJTA8lAXQ0EHF1JrM2OpGc0Fuh2k2/ZxT3Bqw3lHXrOrfDXbdmsJ3GDlMI8FLWt6nUbb/GSGTuaHYq08yATDZsYbGrWtBBxb2QVFD+aHvBOcp54L+qXp1kGNOoEqktL44WAMAOfqdjPe1OyaRdpTPbCg9g+Y0SaPX2Xaxvi6zKZtzTkwZC5zYu/i93W/EhS/GGP0BrKPRrK/SAvOZNDxr0c+h+eGQWUeI4oXEnc0hXdtc1v8EiaEB3G+43lJlZkftuS2aFNVZ+10IuX8Nai1Uj9d+qr/rOXJc0ZyYRvV/UVTkkgJIbrs/in3Y7a5rms1IHqA8/nQ2KE8M+OZVq8TYYpwPr9q+FVaEuRCQmjDPnWRpsg2rzk0bqjz+Ul9TmqSMDTWfH5XoH6m7Tu2B8Zn6jMNznuHqyqyOMNS2bJR2kQSwpIYOG0UO1fdw77afP5s1qrbxIcHsaZ2DwtKtjMwfhTXjxsLgMEykPVH/+m8hKIojGvUI3WebRiz0Xp7okpNrK882XkuISKIdEePlMUewovqGOe5rUeeQWlUn21or0hCjVoP0GC1Ly9O06rTh1QlsL5kkLNd4x4puwr/tI3XVm4Ce9dehOIo/ZBwdCXph77SymoAszKSSYqcw1t7f2Z/JQQlLuSeEy4iPigVnaJoE8zPfBE+0tKkcyb0ZXz/sS1+CfvFhzV5/uJFDW2W7Mrnq41HmmxZ1FmxYSZuO2kwn6w5RE5ZLRabbDDSEZJICSG6bFa6e/NlksKSmNuv5f+8XZmQ3HqdrMaCDcFuX3NQzCAGxQxqvyGB+5mCDwaTGNpQ88dvnymmL8T0xZ1PNGXOOQA0Lq8bf2gJNXu+YO6AM5jbt1ejM0NozXFNXvVqpRVEAk3S1dF/arXtcMdPwzUnuWyna3bNZSXjGF+/UmzhIa0qYq8xAPRPCKd/QjgLCqPZ78gxw6IOcMaQiQ0XGHwypIyG3M2MGjeNUX1a/zwAceFBnNFoE2ydolBYWcfotOg23+euW2YPYuOhEnLKaimrsVBrsRFs7FlFVL1FEikhhBA+Nyt9ltuJXcA76RGYejM0q8v2/MznGffBOAD6RfVr+h6bFfK1SekkDevwLTuyz6O7iqu03spnf85kS3YZb1zuXuJ/rJNESgghhM8dLD/IwXLXm++a9CampExxvv4t97dWhyT7RPahT2QfAAprCtlRtKPVe05OmezcEmlb4TaKa4tdtosLjmN4vNZPVWer4/fc31u02V6zHV22juFxw4kLiYPSwxws2sXB2oY5aDbVhgEdVuw8teapJj2JJZW5KIlRzLYHMahwc6c+067cCvqGjcboKI57sHIXwcE19GpWvsCoMzIhaUJDcdBW/Om4fryydB+7j1awen8RV/1Hm8w/c0giV0zr2+Z7j2WSSAkhhPC5H/b/wKubX3V5LiEkgSUXLHG+vmfFPa1Okv/z6D9z45gbAS05+suSv7R6zyXnL3HORXt186utTvw/Ie0EXpr9EqCtoLxp8U2uL7gYXpr1Eif0PgGWPsYPRet5NSbKZdPdJbvZXbK76cHgYLYCNLt+Rz5T5Z57Ua3aXK2QtHcxROxy2e7K4Vdy+4TbW70OwFljUukVHcL5r62motbK0t0FAPx+oJhZGYnOVZGiKUmkhBBC+Fx6ZDrHpR7n8lx9uYh6k5InUW4ub/U69WKDY1u9JjSdfD88bnirG4QPi2sYajPpTC6vWVxUTGxcLDHB2oo/eo0jXa3iOLVpnHXYqTKFEhbbnyuHX+k8/o9ld5JjqwIgHgMZqQ09cO5+pl25FYwekISBcAB+K07DqqhM7hfrLO+wOmc1AOHGcJfXaG5i31i+u/k4CiprKawwc+cXW6g22zj+6aX88/zRnDs+za3rHEskkRJCCOFzp/c/ndP7n+5W26dmuN7UublRCaN4dY7rXq7m6nt82hMdHO3ymi22JZl9P6cDLj/R4bWw42uos8GAEwGIHXc7F63Vto8pxMoZ/c9gXv95Ld7q7mey2OxMfaKMkiILb94wt0v7G45MiwKisNtVfsksYOXeQspqLOSV17b73mORVDYXQgghvGn/Mlj9UsP2OsCIYefzXOKJztd3rbgLi83S6VsY9TrW/d9JbHrgJI9tEq3TKbx8yThmZ2hzuxIiul5qoSeSREoIIYTwprwt2mPyyCaHZ1VWcn1JmfP1vK/mYbF3PpkCiAhuOqE8qyyLaouLyvMdUFBZB0gi1RoZ2hNCCCG8KW+r9vjj32HBvZA0Aq74Fn3BTi4tr+CXsFB2mYzkVeVR988hGNVG7z35HzDmIu35po/g59b3tPzv8Ut4fpFWtv0120MM4BDn9I3CoigE2VXqp4qr+iAqbeGoBRegqxvsfL+qqiRFBvPenyaRFBnsPF5QoSVS32w8QlyYiVEeql3VU0giJYQQQnhT3EAoOQB1jonotWXOx2i7nafyCzgrrRepFivhVUVN39to6yCstVDd7HwjtRa7sxZUiKmSWF05A82h7AwyUadTqKtvqJrR24OoUnOxVPVtco2Sags7csubJFJ2x/4xX2/KIae0ls9umIpoIImUEEII4U2XfN40AdI5Kob/8QN480QyTdpqwkFmM6RP1Y7XMzVsE8Poi2Doma3e5lJjDOeM0/YzVGonUGK38rJqo8JcgaIoRIU6Vi3qTdQqkQTpmtab2pVXQWFlHSNTm66afOGisby94gCfr88myCgzgpqTREoIIYTwJkWBsPiWx1PHwXG3kbn9HQAGWSyQvxo2vAd6E4w4F4yN3mcM0X5aEQLOsgeEJzmPJzRv+P2tUHEU5jwECQ1De9MHup4DlZEcycS+sXy+PlvmSbkgqaUQQgjhL3MeZM9IrZdpsNkx0XzxI9pcqKWPe+WWO7OWUrB3PmordbRcya/QhhglkWpJeqSEEEIIP9pTok0QHzTtDqguh6yVcGQdhER7/F52m5VLwq1YItL449LbCHZsL1OVMIbMvFj27BvGWWN6YbFXs63mCwD0ip4IyxRAITEiuI2rH5skkRJCCCH8pNJcyZHKIwAc7TeV/ilTUf73Jy2RSuz4ZsbtsViqqF8U+Gnt4YYTB/dhrRxCTcUA3lxxAEVfQfjg752nDTUHgQulR8oFSaSEEEIIP8mvyXc+v37h9cw/Zz5p+Tu1A4lDPX6/oOAoXht1Cztz1jQ5rvYaw9GKBGJHZgBgsdeyteJKcmt3sr/6d0KNIZQACeGSSDUniZQQQgjhJ30j+3LL2Ft4YeMLAIQqRijShvr47ArQGWDcZXDcrR675+Rx1zF53HVNDy59AlY+DLPuh+m3OA4O55VNr/Dq5t8x10UAkBgpiVRzMtlcCCGE8BOdomNuv7kAJIQkEBsaD2HaliyUHoTifbD5E+8HUp4NNjMENd3cOL9K6zGrrtbKMMjQXkvSIyWEEEL4UWZxJgCDYwdrNab+sh7Kc2D3D7DwAa2gp7eVafO0sFng6HatflVMX5buPQhAXW04Rr3CkZJqchQFk15H/4TwNi547JBESgghhPCj3SW7ARgc46jpZAqF+IGw2VEB/fAaeOskCI2F0/4JUWmeD6LSMVdr/p3a48A5cOkXxFdfy8FDZwI6UFVO/fdKAIYkRbDg1hmej6MbkkRKCCGE8KPMEq1HakjMkKYn6otvVuVrPwDDz4bRF3o+iPFXwPr3oH5NX3Q6AA9G/8Si4lper5iKqteRFKUN7dU/CkmkhBBCCL/aXaz1SLVIpI6/AwaeBNY6+Oo6KMmCmL7eCWLy9dpPYzYrozJfZJRq51WmY7HZOVxcA8CRkhpySmvoFd16pfVjhSRSQgghhJ9UmivJrszGqDPSJ6pP05OKAr3GgN2ubekCkJDhs9jyCrZxTWoSQ2wKs0PTKKzUtj3ekl2G2WbnuYWZ3DtvKLFhJp/FFIgkkRJCCCH8ZG/pXgAGRg/EqDO6blSaBdYaiEjxSrXz1uQV7eag0UiEXs/Hl413Hh/10ALMNjv/W5/NXXN9l9gFKkmkhBBCCD+pH9ZzTjR3pUBrQ8KQ1tt4QUHJPgAS9aHOY7UWG+W1VufrK95Zg85RSOnM0b24bsYAALYdKePuL7e0uOb49BgePmuEF6P2PakjJYQQQvhJ/Yq9b/Z9w9aCra4b1Vc6Lz0MO771UWSQX5ENQIIpynmsoEIb3tMp2usdueVsO6L95JbVOttVm23O441/3lt9kMq6hkSsJ5AeKSGEEMJPaqw1zudvbX2Lf8/6d8tGVkeCUrwP5t8Fw870SWz51dq8rKTQBOexAsc8qWEpkTxxzqgm7ePCG+ZKDesVyXc3H+d8bVNV/vDyKhQFQo16b4btc5JICSGEEH5y18S72F60nQNlB0gOS3bdaPpfwW6FFf+EiFbaeEG+TUvgEsJ7OY/V90glRwUzMi3K5fsAwoMMTc7b7CqfXDeFqjoruvrurB5ChvaEEEIIP4kOjiYjRpuwva90H8+tf47PMz9HVdWGRqYwbaI5QOIwn8VWEKXdM3FIQw9YviOR6uhWMXqdwpT+ccwemuS5AAOE9EgJIYQQflRaVwrA73m/83ve7wBMTJpI36i+DY3q50kl+m6V3Ml9TyY9Mr1JWYYCZyIV7LM4Ap0kUkIIIYQf3TflPpYcWoLZZualTS8BEBcS17RRwS7tMXGob4JSVS4YfL5Wy6pxGJ3skdpXUMknaw6RkRzJueO9sMWNH8nQnhBCCOFHfSL7cNWIq5iVPguAtPA0IkwRDQ1UFfJ3aM8TfJRI1ZbCP5Lh1eOaHHYmUuEdTKTyK3lzxQF+3JrrqQgDhiRSQgghRADYVaz1Og2Na5YsVeZDTQkERUFkLxfv9Lyywj38YoR99uomxwsqHBPQO9gjVeGoPRUZ0krR0W5MEikhhBAiALS65159b1RiRouhNm/ZeXQdNycn8lh40zShvkcqsYOJVHmtBYDI4J43o0gSKSGEECIA1PdIZcQ2m1BePz9KVaEg0yexFJQdAiDB0DDEqKqqs45UZ3ukIoKlR0oIIYQQHqaqKrtKtIRpSGyzHqnyHO0xew2875tinEcrjwCQFBzrPFZWY8FiU4kINhDcwaKa5TWOHqkQ6ZESQgghhIcdrT5KWV0Z0UHRJIU2q7U0/koY6kigDL4pO3C0pgCAxLCGWPI7OawHDUN70iMlhBBCCI+rH9YbEjsEpfk8qLgBMOws7bmPCnLmmcsASIlIdx7rbOkDgOhQE71jQ4gLM7XfuJvpeX1sQgghRDfjnB8V00rBTWdBTt+UP8gNiYDqSlJ6TXAe60oxznvnDeXeeT4q3eBj0iMlhBBC+JlzxV7z+VH16hOp7V/B1v95PZ48m1b2ICl5jPNYZ2tI9XTSIyWEEEL4Wasr9uoZHMlL8T5Y8yaMPM+r8Sw8byF5VXnENppsnu+oIZUY2fFESlXVlkOWPYT0SAkhhBB+VGGuILsyG5PO1HR/vcbOfg2m3KQ9j+3n3YCqigjZ8AH9ig6hUxrShK70SE16fDFjHvmZ4iqzx8IMFNIjJYQQQvhRZolWG2pgzECMulZWtRmCwOZIQrw94Tx/B/x4B/SeAv1PcB7ubA0pVVUprTZjsamEmjpWNqE7kB4pIYQQwo/aHdarVz9PKjSu7XZd9PPBhVydnMgXoU1X2OWXdy6RqrXYsdhUTAZdh+tPdQeSSAkhhBB+5Cx90HxrmOaK9miP39wIy57yWjyZpftYExJMblDT1XlHy7U5UkmRHVu1V+HcHqbn1ZACSaSEEEIIv6pPpL7e+zWHyg+13nDMxRAcpT2vyPFaPHk1+QCkhKc6j9WYbZTXWjHqFWJCO5YQlddvWNwD99kDSaSEEEIIv6o0VwKws3gnn+7+tPWGcx6CjDO050kjvBaPsxhndH/nsfoVe/HhQVSbbR26XkNVc0mkhBBCCOFhr8x5heFxwwHoFd6r7cZHt2mPScO9Fk+uXUuakuMa5mzVbw+TW1bLiIcW8N6vWW5fr37D4siQnjm01zPTQyGEEKKb6BfVD6tdSzaGxbWxIs9ugwJtGNBbK/dUVSVPUQFIThzpPD4wIZwhSREcKKrCbLWzI6fc7Wv2jw/joTOGdXhuVXchPVJCCCGEH9XZ6thXug8Fpe0J58X7wVoLkWkQEu2VWIprizErEGmKJDQixXk8JszEgltncMMJAwBI6kBRzt6xoVw5vR+njkxpv3E3JD1SQgghhB/tKdmDVbXSP6o/ocbQ1hv6YFhPReX8weejV1yXKShwzJVK6KG9S50hiZQQQgjhRzuKdgAwNK6dTX2Pau28mUjFh8TzwNQHWg/BUUtq46ES0qJDODEjsd1rrj9YzIHCasamRzMgIdxjsQYKGdoTQggh/Kg+kRoW2868p6PbtUcvJlKsfRv+PQZWv+zydK1FW7H35YYjXPXuWvIdtaXa8uWGI9zx+WZW7in0ZKQBQxIpIYQQwo+ciVRbE83BJ0N7e49uYmdVNtWOkgzN3XPqUK45rh8GnbYBsTsbEZfVaOUPojtYf6q7kKE9IYQQwk8sNgt7SrWK5XtK95BblQuAXtFzXNpxRJoitYZ1FVB6EPQmiBvotXheKdnEwtQUnlKrmefi/Mi0KAYnh/PWygPodQpxYSYXrZqqT6Sk/IEQQgghPCq/Jt9Z+uDx3x9vcu7MAWfyj+P+4Wjo2GcvYQjovZeQ5FkrQQcpsa0nawWOmlIJ4UHodO33SJU7EqkoSaSEEEII4Um9wnpx85ibySrPch77fv/3AHy771sUFB6d/ihK/fyoRC/OjwJyVQugkLx7IQw+C4IdPWJr34aC3TDyfPJVLclKdLMEQmn90J4kUkIIIYTwJEVRuH709U2OTUqexAO/aivnvtn3DfdOvpdQH0w0N9vMFOlAp6okbP8G5j7TkEhlLoA9C+DoNvInvgNAYoR7iVRZD++RksnmQgghRAA5e9DZvDbnNQCSw5K12lI+SKRyq3JRFYVkYySGU5+GoEalClLHa4+mcOe+ewkR7deSUlXVuTefzJESQgghhE+U1pUCkBGbAaoK+fWJlPc2Kz5SeQSA1PgMmNy0l8xZST0qlXxHLSl3qpsrisLuR+dSZbZh1PfMvpue+amEEEKIbmxXsbanXpQpim1Zi9ltr0YNjYPw9gtgdlZ9ItUrzMXGyWXZ2mNkL2ePVKIbPVKgJVPhQT2336bnfjIhhBCimzpQdgDQ5kh9s+8bSE3hRmL4sxt1mzrrrAFnMSl5EjrFRR9LeY72GJlG/j6tR8rdOVI1Zht78itcnkuICCIlKqRT8QYKSaSEEEKIAHPmgDMpqS3BptrILdlLsb2OmrA4r97TpDfRJ7KP65PlWm9V46G9t1bu58uNWk9V75hQ7pybgb5ZOYRNh0u5+4st7MpznUjdcMIA7j41wzMfwE8kkRJCCCECzMl9T+bkvicD8Jf3p7GMOoYmjPZfQKc/D8UHIHkkJoNWif23/cVNmpw1JpVhvSKbHBuUGE56bGir86OS3SyhEMgkkRJCCCEC2A5bOegUhqaf4NX7/GXJX4gwRnDflPsIM4Y1PZk4VPsB3rx8AmsONCRR9361lbIaCxHBLVOKsCADb1w+watx+5tMNhdCCCECVGHxXvJ1CqF2O+npx3ntPrXWWpYdXsb8rPkE69ueRJ4QEcRpo1I4bVQK80YmU+MobxAf3vHepeveX8c5r6zicHF1Z8IOCNIjJYQQQgSoXfsXApCBCZ21Dqza/CQUBUxhbbyzY3IqtcnkKWEp6HX6picL98Bvr2i1pMZe2uRUeY0Vs81OeJCBEFOz97lh25Eycspq8eIceq+TREoIIYQIUDvz1gEwrKIYnkhtenLaLXDyox65j7P0QbiL0gf5O2HdO1Ce2yKRKqh07Lvn5gq+5irrtH0Gu3N5hO4buRBCCNHD7dRpicZQmwL185bsFrCZIW+rx+6TXamtvksLT2t5stGKvebqNzCuNlt57ufdzuMnDElgfJ/YNu+pqipVjmHBMEmkhBBCCOFpO2qOAjD0yoUQM0g7uPQJ+OVJSB7psfscKj8EQO+I3i1POotxtkyk7KoKwNHyOl5Ystd5/OtNOSy/88Q271lntWOzq5gMum5d9VwSKSGEECIAldWVcaTyCEH6IPpF9Ws4kbdFe0zxXDmE7AotWUqPTG950tkj1bK3alK/WJ44Z6SztlRBZS0f/HYIg679SU8VtVpvW0Q37o0CSaSEEEKIgLS7WBsqGxwzGIOu0T/X9UN6HuyRGp04GptqY0DUgJYnyxyJlIseKaNex0WTGpKv3/cX8cFvh4gNM7V7zyrH/KjuPKwHkkgJIYQQAWln8U4AhsYObThYXQxlh8EQAnEDPXava0ZeA63lZfU9UpEuJqI3U1RlBnArkQox6bl0SjpRIUZ3wwxIkkgJIYQQAWhHkVZBfGhco0SqvjcqaTg0L1PgDaoK8YPAENShRCrOjZpSSZHBPPYHz/Wq+YskUkIIIUQAcvZIuUqkUkZ57D6FNYUcrTpKemQ6EaaIpicVBS7/xu1rFTnKIRRU1PLTtlxOGJzYqfpS3Un3nSYvhBBC9FDVlmqyyrIwKAYGRQ9qOFE/0dyD86N+OfwLF/5wIU+uebLL16qvcr5oZz43fLCBl5buabVtfkUtmw6XkldW2+X7+pMkUkIIIUSA2V2yGxWVgTEDMekbzTdyTjT33Iq9QxVtlD6oqwSz+9u3/HFiby6YkEZGstazVWext9p24Y6j/OHlVfx7cWbHAg4wkkgJIYQQAWZ74Xag2URzSw0U7AZF59xA2BMOVxwGWkmk1r8Lj6fAwgfculb/hHCePm80JwxOACCmjUnnzlV7pu49y0gSKSGEECLAbC3Uep5GJjQawsvfCaoN4geDKdRj96pPpNIj2qghFRrXoWs6J523kUhV1vaM8geSSAkhhBABxplIxTdKpLwwP0pVVWdVc5fFOEu1c66KcbalxJFItdUjVVmnzaeKCO7eiVT3jl4IIYToYUprSzlccZhgfTADohsVyHTOj/Lcir2i2iKqrdVEmCKICopyEcxB7TG6b8eu60ikSqvNbDtSBmjFOwcnhaMoWtVzKcgphBBCCI/bVrQN0MoeGHWNilXmer5Hyrk1jKthPWjokYpu5XwrymstANz1RdONlW8+cSB3nDIEgEpJpIQQQgjhafXDeiPiRzQctNvgqDYB3ZM9UiPjRzL/nPlUW12szKsphdoyMIZCWHyHrnvF1L58uvaw83VxlZm88lqOljeUOqhPpMKDunedKUmkhBBCiACyrVDrkWoyP6poH1iqtP3uwjo28bstep2etIhW5j+VORKh6HStMGcHXDGtL1dM6+t8/fov+3hi/i6iQxt62B4/ZyTFlWbS4zw3cd4fJJESQgghAoSqqmwtcNEjlbNBe+w11nfBRKfDxZ+B2notKHeVVGtDfdGhDZPPU6NDSI0O6fK1/c2rq/YURZmrKMpuRVH2KopydyttZiqKsklRlO2KovzizXiEEEKIQHak8ggldSXEBMWQFt6opyhno/bo4UTqoV8f4u4Vd5NXldfyZHAUDD4Fhpza5fuU1WiTzxv3SPUUXkukFEXRAy8DpwLDgIsURRnWrE008Apwpqqqw4HzvRWPEEIIEejqh/VGxI9wrm4D4IijRyp1nEfvt/jQYn7Y/wN6xbvzlErre6RCGnqk7vlyC/d8uYVqs9Wr9/Y2b/ZITQL2qqq6X1VVM/AJcFazNhcDX6qqeghAVdV8L8YjhBBCBDSX9aNsloYaUiljPHavsroySutKCTGEEB/iYjL5mjdh+TNQerjluQ4qqW7ZI/XF+iN8vOYwug7Ovwo03kykUoHGv/rZjmONDQZiFEVZpijKekVRLvdiPEIIIURAa9wj5VSwC6y1ENMPQmM9dq/60ge9I3o37f2qt/49WPIYVBV0+V71PVJRIVoiVWe1YbbZMegUggzduza4Nyebu0oxVRf3Hw/MBkKA1Yqi/KaqapMdDBVFuQ64DiApKYlly5Z5PlqHyspKr15fuE++i8Ah30XgkO8iMHjje7CpNudE87LdZSzbq10/JednhgBHjWns9OA911etByC4NtjlZzmucD8GYNX2bCx7yrt0r6MlWnmF3VvWU7hHR4VZSweC9Cq//NK16dH+/jPhzUQqG2i8A2IakOOiTaGqqlVAlaIoy4HRQJNESlXVN4A3ACZMmKDOnDnTWzGzbNkyvHl94T75LgKHfBeBQ76LwOCN72FX8S4shyykhadx+uzTG0589xUASWNOIWma5+65c9NOKISJAyYyc3yz69aUwrIqMIYx/aQzOlz+oLmaxfMBlbmzZhAWZOBgURUsWUZMeEiXfx39/WfCm4nUWmCQoij9gCPAhWhzohr7BnhJURQDYAImA897MSYhhBAiIG0p0OZBZVdm88muTwAYHDOYcc4Ve56daH6g7AAA/aL6tTzZuKJ5F5OoWouNWosdo14h1KRNaq9wbFgcEdz9V/F5LZFSVdWqKMrNwAJAD7yjqup2RVFucJx/TVXVnYqi/ARsAezAW6qqbvNWTEIIIUSgyq7Mdj7/x+//AECn6Fidf4RQFEgZ7dH7jUoYRbW1miGxQ1qe7OTWMK6UNqohVT8Xy5lIdfPtYcDLBTlVVf0R+LHZsdeavX4GeMabcQghhBCB7oLBF2Cz26iz1VFWV8ZPWT8Rqgsi2GaBhAwICvfo/S4ddimXDrvU9UlPJlL1NaRCGnqfgo06JvaNYVivyC5f39+6fyoohBBC9ABpEWn8feLfAVh4cCE/Zf3EyOB4dOz2+LBeu/RGiO0PcQO6fKmGHqmGRGpsegyf3zANgLdXHuCp+btavE9FJSUqhK9unEZceFCX4/AWSaSEEEKIALM5fzMAoy2OAx4uxJlXlUdeVR79o/sTaXLRKzTpWu3HA0odNaSiGhXjbMxuVzHbXG9D0ys6mNyyWkmkhBBCCOG+zQVaIjWqzFGn2sNbwyw6uIin1j7FBYMv4P6p93v02s3V90itzSrmjBdXMntoIn+bM9h5/qrpfbl8Wh+X7zXqdOh0gV2ws3tXwRJCCCF6GIvNwo6iHQCMyt8HOiMkjWjnXR3T5oo9ux1qu1Y3qrGkyGAAymosbD1SxpvL9zc5b9DrCDLoXf4EehIFkkgJIYQQAWVX8S7MdjP9QpKIstsheSQYgz16jwPlbSRS5Ufgyd7wylSP3OvEjER++ftM3rx8AuB+yYOj5bUUVtZhszev5R1YJJESQgghAkj9sN5ovWOVXtpEj9+jzR6pYkePUZDnVtT1iQsjJUpLBhtPOm/L2S+vYsJji8gtq/FYHN4giZQQQggRQJyJVI22rYqnE6lyczmFNYUE64NJDktu2aBES7KI7e/R+7pavdeWWqs2AT3YqPdoHJ4miZQQQggRQJyJ1FFHz1DaBI9eP6ssC4A+kX3QKS7SgPoeKU8nUo56UjGhrlfvNVdrsQGSSAkhhBDCTfnV+eRW5RJmCKF/RYF2cPmzkOe5TT+yyrOAVob1AIrre6RaOd9JJR3okVJV1ZlIBRkCO1WR8gdCCCFEgKjfb29k5AD07NYObvoADEFw+nMeucfp/U9nUvIkLHaL6wZeSqTKHPWkot3okbLYVOwq6HUKRn1gJ1KBHZ0QQghxDHEO66VNhyt/hFTHsF7cQI/dQ6foSA5LpndE75YnVbVhaC/GOz1SMW70SNVZHcN6Ad4bBdIjJYQQQgQMZyKVMBrSpkOdo55T78m+CUBV4Y/vQ8lBCI316KVL6nukWqlw3litpXtMNAdJpIQQQoiAYLaZGwpxJoyC6mIozARDsFZLykP3uPTHSxkUM4jHpj+GojQreKnTwcA5HrlXc2UdmCMVEWzgP1d5vuyDN0giJYQQQgSAHUU7qLPVMSBqAFFBUZC5QDvRaxwY3Fvp1p4DZQfYWbyTGmtNyyTKy0o6MEcq2KjnxCGJ3g7JIwJ/8FEIIYQ4BmzM3wiAoih8u+9brIdWayd6T/LYPfaW7gVgYHQrc652fAvLnoK8rR67Z73SGvfnSHUn0iMlhBBCBICcyhxAS3buW3kf/dRkRoJHE6k9JXsAGBjTWiL1DWz7H0T39thwYr2Ggpzt90gdLq7m07WH6Rsfxnnj0zwah6dJj5QQQggRAP404k/cMPoG5+ukXG2+FGme75EaFD3IdQMvrdiz21VKHUN7USHt90gdKq7mpaV7+WJ9tkfj8AZJpIQQQogAkBKewom9TwQgLSSRxLpKrbp4eILH7uEc2nPVI6WqULxPe+7hquYVdVbsKoQHGTC5UdKgvvxBkDHw05TAj1AIIYQ4Rmw4ugGA8aY47YAHyx5UWao4UnkEo85IekS6iwaFUFumbVYc7tmJ3h3pjYJG5Q8MUv5ACCGEEG5af3Q9AONra7UDHtyw2GKzcM3Ia6i2VGPQufjnv0ibP0XcQPDwir76+VExYe4mUvX77AV+f48kUkIIIUQAUFWVDflaj9S4fMc2LR7skYoOjuav4/7aeoNCRyIV38r8qS6oL33g7obFddbuU5Az8FM9IYQQ4hhwoPwAxbXFxAfFkl58CEwRkDjUdwHoDBA3yCv3rO+Rcn9or75HKvATKemREkIIIQJA/fyocaHJKKCVPdB5LpFYdngZUUFRDI8bjknvomdo7CXajxeUdrBHKsigJzky2K0q6P4miZQQQggRAJyJlFkb1qLPNI9e/8FfH6S4tpifz/2ZlPAUj167PSUd2B4G4OLJ6Vw82cWE+ADk1tCeoijTFUVZqChKpqIo+xVFOaAoyn5vByeEEEIcK+onmk8oPKgd6Hucx65dVFNEcW0xYcYwksOSWzaw26CqyGP3a660A9vDdDfu9ki9DdwKrAds3gtHCCGEOPbkVuaSU5VDhDGMgQd2ahsV9xrrses33hrG5R57xfvhpQlaNfMbVnrsvvXqe6Ri3Vy11524O9m8TFXV+aqq5quqWlT/49XIhBBCiGPE+nytN2pMaBp60MoeGII8dv1299irX7EX5rnin40VV2k9UrFh7n2mh7/bzoTHFvL1xiNeiceT3O2RWqooyjPAl0Bd/UFVVTd4JSohhBDiGOIsxGlz9G94eH5U/R57g2JaKW3grCHl+dIH0CiRcnNor7TaQmGlGatd9Uo8nuRuIlVfyGJCo2MqMMuz4QghhBDHnjV5awCYUOTogfFwIpVZkgnA4JjBrhsU7NYevVBDChrqSL20dA/RISYum9qHEalRrbav3yKmxxTkVFX1RG8HIoQQQhyL8qryOFh+kDBDKMOzdmj1nDxY0VxVVY5WHwXaSqR2aY9eqltl1GsJ0YLtWhxFVXU8ee4owkwGQkwtSzz0uC1iFEWJAh4EZjgO/QI8oqpqmbcCE0IIIY4Fv+f+DsCEiL4Y1F2QOh5MYR67vqIoLDpvEUerjxIV5KIXSFUbeqQSvJNIvf+nSazNKmZdVgmfrjvMop35THhsEWEmPT/ccjx945t+3p5YkPMdYBtwgeP1ZcB/gHO8EZQQQghxrKgf1ptsd/yTbK6GBfdpz4MiYdK1EBrbpXsoiuK67AFA2WEwV2oTzcPiunSf1vSND6NvfBgT+say/lAJJVVmymosVJltHCyubjWRCuopQ3vAAFVVz230+mFFUTZ5IR4hhBDimKGqKr/l/gbAJKsjacjfrv3UC43Vkqku3GNN3hre2PKGy/OTEsdy/eXfQq33B5n6xYex6LYTADjzpZVsyS4jMrhlKtLjhvaAGkVRjlNVdSVoBTqBGu+FJYQQQvR8B8sPkl+dT0xQDINOehJSp4Ldqp1c9W+oLoTYfl26x2tbXmPRwUXsL92PVbW2OB8XHAdjb+7SPTqjrKb1/feumNaHI6W1JEV5rgSEt7ibSP0ZeM8xV0oBioErvRWUEEIIcSyoH9ablDIJXVQqTL1RO1FbDoseAkUPvad06R7XjbyOKSlTMNvMLs/HBndt2LCzyh2JVKSLROqPE7vH9jDg/qq9TcBoRVEiHa/LvRmUEEIIcSxwDuslT2p64vDvoNq01XtB4V26h16nZ2xi61XSn/viXAw6AxfPeIx4L9WRak5VVcprtd6xyODuXe28zURKUZRLVVX9QFGU25odB0BV1ee8GJsQQgjRY9lVO2vz1gIwJaVZr1PWCu3Rg/vtuaLa7XxevotKnY5LFN9N7K4y27A5im1O/MciDDqFv58yhAsnaT1R32/JIdigZ1ZGIjqdiy1tAkh7v2r10+gjWvkRQgghRCdklmRSWldKclgyvSN6Nz2Z5djvzsuJVF7eBip1OmLtKnGxA7x6r8ZCjHoGJGgpRlmNhaIqMwt3aDWmbHaVmz/ayLX/XYerbQEDTZs9Uqqqvu54fNg34QghhBDHhvr6UZOTJzfdSLi2HHI2eWR+VHv2HF4FwCAlxKv3aU6vU/j51hOoqLXw3ZZc7v96G+GO1XvVZm3IL9Sod73BcoBxqx9PUZSnFUWJVBTFqCjKYkVRChVFudTbwQkhhBA9lTORSpnc9ET9/KjUcV2eH9WePQWbARgYmuTV+7ii1ylEh5qwO4b4IhyJVI1ZqyEVYnJ3PZx/uTsgerJjgvnpQDYwGPi716ISQgghejCLzcKKI9o8qGpLNWV1jWo4+Wh+FMDe8iwABkX7ZpK5KxW12uq9CMek82pHIhXqYuuYQORuIlU/pX4e8LGqqsVeikcIIYTo8Q6WH3Q+f+z3x/jr0r82nPTR/CiAPXXaP+cDk8d5/V6tqXCs3otwDu31zETqO0VRdgETgMWKoiQAtd4LSwghhOi50iPTuWzYZfSL0opthhsdQ3g+nB8FMDQonkE2hQF9Znr9Xq0pdyZSWp9NjUV77Woz40Dkbh2puxVFeQooV1XVpihKFXCWd0MTQggheiaT3sSdE++ksKaQA2UHmNprqnbCg/Wj3PHoxYu8fo/2VNbV15Nq2iMV0g02LIb260jNUlV1iaIo5zQ61rjJl94KTAghhOjJ7KrdOeHcmUgdWK49+mBYL1A0zJHSUpKp/ePY9MBJOOagB7z2eqROAJYAZ7g4pyKJlBBCCNEpO4t2UlxbTEpYCv0iHfvp7V+qPfab4fX7Hzr8K6agKJIShvm1zEBFs6E9g15HdKjJb/F0VHt1pB50PF7lm3CEEEKIY8PyI1rv0/Gpx2uJTGUB5G0FfRCkT/X6/Z9bdieL7WU83fdcTj3hIa/frzX1PVKl1Rb2FVSSGh1CcDcZ1gM350gpivI48LSqqqWO1zHA7aqq/p8XYxNCCCF6rJXZ2uq849OO1w4c+EV77DMNjN4vkLnXUgZ66N9rotfv1ZaqOm1O1LXvrwMgNsxIRnIkZ47u5dwyJpC5u2rv1PokCkBV1RK0UghCCCGE6KCS2hK2Fm7FqDM2bFi8b4n2OOBEr9+/pqqAQzoVg6rSP32m1+/Xlosnp9M/Pox+8dqWMcVVFn7dV8Sh4mq/xuUud8uG6hVFCVJVtQ5AUZQQIMh7YQkhhBA916qcVaioTEiaQKgxFFQV9jnmR/X3fiK178BiVEWhj12HMSis/Td40U0nDuSmEwdSa7GRcf9P6BSwq6AooKpqwG8T426P1Ado9aOuVhTlT8BC4D3vhSWEEEL0XCuyterlzmG9wkyoyIGwBEga4fX778xZDUBGUJzX7+Wu+rIH9av1Xl66j6vfW+fHiNzjbh2ppxVF2QLMARTgUVVVF3g1MiGEEKIHstltrMrRNgs+PtWRSDl7o2aCzt0+js7bVbwbgKFRA7x+L3fFhBqZMTiB1fsKsdi0bGpLdql/g3JDR76tncBPqqreDqxQFCXCSzEJIYQQPda2om2U1ZWRFp5Gn8g+2sH6+VE+GNYD2FWTD0BGygSf3M8diqLw/p8mccrwZOex7rB6z91Ve9cC1wGxwAAgFXgNmO290IQQQoiep/GwnqIoYDU37K/ng4nmAK+f8w27Dyxk6MDTfHK/jqhxDPFB96hu7m6P1E3AdKAcQFXVPUCit4ISQggheqoVRxyJVP2wXvZasFRBQgZE9vJJDOFRvRk/5k+Ehif55H4dMb5vDFP6xwLdY789dxOpOlVVzfUvFEUxoFU2F0IIIYSbCmsK2VG0gyB9EBOTHfWbfDysF+hunDmQv84eDHSPoT13E6lfFEW5FwhRFOUk4HPgO++FJYQQQvQ8q45ok8wnJk8k2BCsHdzjWLs1aI5PYnjru6u4+b/TWLvpHZ/crzNqLd1n42J3E6m7gAJgK3A98CMgVc2FEEKIDmgxrFeeo20LYwyFPr7ZqHh10VZ+sVdQVVvik/t11IHCKg47inGGdoOhvXYnmyuKogO2qKo6AnjT+yEJIYQQPY/VbuXXnF+BRonUnoXaY78TwBjs9RhUu52dai0oChn9TvL6/Trj/Nd+pbBSm03UHXqk2k2kVFW1K4qyWVGUdFVVD/kiKCGEEKKn2Vq4lQpzBQA3L7lZO1h+hAGJ8Tw9cLbbW410Rc6R36jQKcTYVZISR/rgjh1X3WjV3sKdR5nz3C/oFYU/zxzAH8am+jEy19z93lKA7YqirAGq6g+qqnqmV6ISQgghehiTzoRBMWBVrewv2+88vj8slNL0ycT7IIYdWYsBGKoPD8itV+x2lRpLQyJVUWulorYSgM/WHe7WidTDXo1CCCGE6OGGxw9n4fkLKasrA2DD9k94ZO8n9LcrxCeP8kkM245uBGBERF+f3K+jqi02VFWbG7Xk9plU1Fr47UAx93+9DZPB+xXfO6PNREpRlGDgBmAg2kTzt1VVtfoiMCGEEKKniQ+JJz5E63v6X84aAGZGDvLZ/bdVHgIFRiQHTkXzxqrqtBQjPMhAclQwyVHB7CvQeqSCAjSRai+q94AJaEnUqcA/vR6REEII0cOpqsqySm14b+ZA382SOSV5Kqfqoxk5cJ7P7tkRFbUNiVS9WosdgCBDYE48b29ob5iqqiMBFEV5G1jj/ZCEEEKInm3/oV/I1kGMzc6oYRf67L4XzH2RCyy1sPwZ4AvXjYafDckjfBZTY/U9UmGNEqk6qzZnKlB7pNpLpCz1T1RVtQbixDQhhBCiu1m69X0Ajg9KQG8M8u3NbWZY8Wzr5xOH+i2RGpAYzqfXTcGgb0ia6qxaj1SgVjlvL5EarShKueO5glbZvNzxXFVVNdKr0QkhhBA90C+Fm0CBmX18V8tpRfYKVFTGxQ0n/MQ2amonDfdZTM2FBxmY3D+uybE659BeN+yRUlU1MNM/IYQQopsqKtjJZswYVZg25hqf3ffVza+ytXArb578JlNO+LvrRtXFsOdnQIHEDJ/F1hbn0J4xMBOpwIxKCCGE6KFWbHoTVVGYpIsgLDzJJ/e02CzsKt4FwLC4Ya03PLIBvroefrjdJ3E19+u+Qh79fgeLdx51Hqsf2gvUyeaSSAkhhBA+9Itjm5gTUqb67J6ZpZlY7Bb6RvYl0tTGrJyj27RHPw3vbTpcytsrD7Amq9h5rCGRCsyUJTCjEkIIIXqguso8Vtm1bWJmjr3WZ/fdXrgdgBHx7Uwir0+k/DTZvLK+/IGp0ao9S2Cv2gvMqIQQQogeaPWGN6jR6RiKiZT4oT677+aCzQCMjG9nf708//ZIuS5/4OiRCtBVe5JICSGEED6y8OAiAOYkjPfpfTfma1vDjE0c23ojax0UZoKig8Q25lF5UWWd1vsUHty4IKd2LFgmmwshhBDHLktNKUutRQCcNPpqn9232lKNXtETaghlUEwb29EU7ALVBnEDwRjis/gaq6zTyleGu+qRCtDJ5u5uWiyEEEKILvh901tU6HQMtOvplzrZZ/cNNYby3dnfUWWpwqBr45/9siOgD/JrHamq+h4pl4lUYPb9SCIlhBBC+MCiA/MBOClulF/uH2YMa7tBxjy4Nwfqyttu50WJkUH0jQslOtToPNawRYz0SAkhhBDHJKu5msW1eaDXMWfkFT69d2ltKdHB0e411hsgNNar8bTluQvGtDjmrGwuc6SEEEKIY9P6Le9RqtfR164wqO8sn93XbDMz6/NZzP1iLmabufWGqgp2m8/i6ohAH9oLzKiEEEKIHmTh3m8AmBOVgaIoPrvvjqIdWOwWQgwhmPSm1hsW74cnesOnl/ksNldUVW1xLNCH9iSREkIIIbzIbrOyuDobgJOGX+LTe9fXjxqdMLrthjkbwVIFbfVaeZmqqgz+v/mMfGgBNntDQhXoPVIyR0oIIYTwovVb3qNQr5Bqg6GDzvDpvTflbwJgTOKYthvmau1IaaedF9VYbFhsKnqdHb2uodeufo5UsBTkFEIIIY49P+z6FIB50RkoOt/9s6uqqrMQZ/s9Upu0x15jvBpTWyodVc0blz4AqLXKFjFCCCHEMclSV8XC2hwA5o323d56AAfKD1BUW0RccBx9I/u23lBVIXeL9tyPPVLlNVoiFRlsbHJcVu0JIYQQx6hVG1+jXKcw2K5j4ICTfXrvdXnrAJiYPLHtCe7F+6GuDMKTIDLFR9G1VFajVTWPCGlIpFRVdU42N+kDM2WROVJCCCGEl/y491sATo1vY487LzljwBmkR6YTZminEGcAzI8CKK/VEqmoRomU1a5iV8GgUzBIIiWEEEIcO6or81lqKQKdwqnjb/L5/UMMIUxJmdJ+w96T4YwXtB4pPyp39EhFBnef7WFAEikhhBDCK5ase5FancJY1URqr4n+Dqd1UWkw3rfV1l0ZmRrFI2cNp3dsqPNYncUx0TxAV+yBJFJCCCGEV8w/tAiAecnTfH7v7/d/z89ZP3Pe4POYkTbD5/fvjP4J4fRPCG9yrL5HKjiAe6QCNzIhhBCimyop3sev9gr0qsrJk/7q8/svz17O0sNLya3Mbbth0T746R7I/Nk3gXWQc2gvgHukvJpIKYoyV1GU3Yqi7FUU5e422k1UFMWmKMp53oxHCCGE8IWf1jyPVVGYooQRGzvQp/dWVdW5Ym9C8oS2Gx9cBb+9Aps/8kFkbVuxp4DP1h3mcHG181itY2jPoFOoqrNSVWd1ruILFF4b2lMURQ+8DJwEZANrFUX5VlXVHS7aPQUs8FYsQgghhC99nbsSdHBW31N9fu+D5QcpqCkgNjiW/lH9226cvVZ7TPP/HK6P1xzix615vHzxOOc8qfpEak9+JcMf1NIEk0HH21dM4PhBCX6LtTFv9khNAvaqqrpfVVUz8Alwlot2fwG+APK9GIsQQgjhE5l757NDZyPCrjJr8q0+v//ao1pyND5pfPsbJGdrPVeBkEjV15GKDGno4xmYGM7gpHBCTXpCTXoMOgWz1c6W7DJ/hdmCNyebpwKHG73OBiY3bqAoSipwNjAL8P+3KIQQQnTRNxtfA+DUkDSCgqN8fv81uWsArRBnm2rLIX8n6IyQPMoHkbXNVWXziGAjP996gvP1o9/v4O2VBwKqOKc3EylXabDa7PW/gLtUVbW1lTUrinIdcB1AUlISy5Yt81CILVVWVnr1+sJ98l0EDvkuAod8F4Ghte/Bbqvj+4q9oNeRYZrm8+/KrtpZnr0cAP1hPcvyWr9/dMlmxqBSHtaXDat+81GErTtaos2N2rllAyX7XCdKWYfqtMcD+1hmPwT4/8+ENxOpbKB3o9dpQE6zNhOATxxJVDwwT1EUq6qqXzdupKrqG8AbABMmTFBnzpzppZBh2bJlePP6wn3yXQQO+S4Ch3wXgaG172Hp6mcp1usYYFM47w//59NNigHqbHXcknkLO4t2cv7089se2vtFGwKMHDY7IH5PmZf/DFg4eeZxxIaZXLZZULwVDh1i6JDBzJzSB/D/nwlvJlJrgUGKovQDjgAXAhc3bqCqar/654qivAt83zyJEkIIIbqLr/d+BcBZiRN9nkQBBOmDuGToJe41DouDlNFaZXM/U1XVWdk8Irj11MRi08ohHBNDe6qqWhVFuRltNZ4eeEdV1e2KotzgOP+at+4thBBC+FpRYSbLbWXogTMm3+7vcNo34U/aTwCoNtvQ6xSC9TqMbSRJ9YmU0dDOJHof8mplc1VVfwR+bHbMZQKlquqV3oxFCCGE8KYf1vwTq6JwgkUhPm8HBMVAZIrP7l9lqeKtrW8xvdf09utHBZiwIAOZj53qLMDZGqtNm2pt8ENvX2sCJxIhhBCim1JVla8KtDlHZxXnwxdXwzc3+jSGtXlreWvrWzy/4fn2Gxfu1aqaq83XgPmPoigEt1PB3FzfIxVAQ3uBE4kQQgjRTW0u2MxeLMRiYKYSoR1MyPBpDL/m/ArAtF5u7O234p/w4jhY97aXo/Is5xypABrak0RKCCGE6KLPdn8GwDlDL8ZocWxxMu4Kn8awOmc14GYidXCV9hgAhThB2x5m7r+W89RPu9psVz+0Jz1SQgghRA9RWlvKgqwFKCicaw8CcyWkT4VE3/VIHak8QlZ5FuHGcEbEj2i7cVk2lB6EoEhIaqetj+SV1bIrr4KjZbVttqsf2gukOVJenWwuhBBC9HTf7PsGs93M9NTppG39Wjvo496o33N/B6DGWsPekr0MjRsKwD/X/ZNv933bpK3VUk1EWi8eCerHJF3bc5J8pbxWq2r+/dZcfskscB5fdfcs57ypP727lg0HSwC4/r/rnL1S4Xor302xNKmI7kuSSAkhhBCdpKoqn2d+DsAFiZNh5ccQHAXD/+DTOOJD4jHoDFjtVmyqzXm8ylJFcW1xi/blRgOboxOZ5Msg2zA4KRyjXttHr8hqdtmmvMaC1a4N7dUnXgBFwLbsMqYNjPdFqC1IIiWEEEJ00pq8NRwsP0hiaCIzDm3VDo66EIwhPo1jRtoMVvxxBXW2OiKDIp3Hbxt/GzeNualJ26J357Kt6ghjJ5/j0xjbcvygBDY+cDK1FluT40GGhiG8t6+YyLmvrmJvQRUfXTOZwckRfLc5h6279pAeF+rrkJ0kkRJCCCE6qX6S+Xn9z8Lw87PawfG+HdarF24KJ5zwto/VlhNXdJjBAAPn+jS+9oQHGQgPaj0tiQo1Ul9lKjEyiPjwIK6a3o9lloOkxUgiJYQQQnQreVV5LDm0BL2i52yLDurKtFVwScP9HVrrgiPh7kNQtBcMrvezC2Syak8IIYToIT7d/SlW1cqc9Dkkb/xEOzjxWv8G1Y5XN7/KA78/Rqape/aj1NeRMjgSqdX7iliTa6Wgos5vMUkiJYQQQnSQ2W52TjK/NHY0HN0KYQk+n2TeUT/u/5Gv9n5FdX2tq27GudeeXivI+cLiPbyyuY49Ryv8FpMkUkIIIUQHra1aS1ldGSPiRjB650Lt4PgrwRDk17jakl+wg6zyLELQMTxumL/D6RSLY2jPJEN7QgghRPekqirLKpYBcGnf01B2fQ86A0z4k1/jas/aHZ8CMJ5gjPruNz8KWg7tBYLAiUQIIYToBlbnribPkkdiSCInH90Hqg2GngGRvfwdWpvWHNH24psUFxjVzDuj+dBeIJBESgghhOiAD3Z8AMCFg8/DuOG/2sFJ1/sxIjeoKmtqcwGYNOhMPwfTOaqqOof2jAG0RUzgRCKEEEIEuANlB1hxZAVGxch5tiCoKoCkkZA+xd+htSnn8Cqy9QoRdpWMAfP8HU6n1Fc1N+gUdLrA6ZHqnusfhRBCCD/4aOdHAEwMnUjM2v9oBydfD0rg/MPuivHwGq4rKcOamIHe4J896bqqYX5UYP1aSyIlhBBCuKG0tpRv9n0DwOn0grxPtJIHI8/3c2TtSzi4hr+UlsEJl/s7lE6zWFsW43zrigksW76Cif1i/RWWJFJCCCGEOz7a9RE11hqmp05n6s4V2sGJ14Ix2L+BueOUf8DA2TBwjr8j6TSLXeuRalz6ICzIQJhR8Wulc5kjJYQQQrSj2lLNhzs/BOCatJOIL1oLhuCAL3kAsLNoJ+/lrSRryEkQkezvcDotUIf2JJESQggh2vF55ueUm8sZkzCG8Tt+1g6OvRTCE/wbmBvmH5jPs+ue5Ys9X/g7lC5xNbT32Pc7eGpNDduOlPkrLEmkhBBCiLaYbWbe3/4+ANf0Pwtl2/9Q0cG0v/g5Mves3KHtA3hc/Gg/R9I1rob2tueUs7PYTnmNxV9hSSIlhBBCtOW7fd+RX5PPoJhBzNi/BuxW8hOPg5i+/g6tXXlHt7JHrSHEbmds0gR/h9MlMrQnhBBCdDM2u43/bNfKHFw96I8oG7SeqUPp5/ozLLet2vIuAJP1kZhCov0aS1e5GtoLBIEVjRBCCBFAFh5ayMHyg6SGp3JK7h6w1sCgU6gK7+vv0NyyMvc3AI5LnuTnSLqufmhPEikhhBCiG7Crdl7f/DoAVw25GMOaN7UTg08hqDbfj5G5x1JXwW+2UgCOG3mFf4PxALM18PbZA6kjJYQQQrj088Gf2Vu6l6TQJM6uU6G2VDvxw21MBRgYp9VmClAVe39mak0tR0IiSE0Z5+9wuqw+kQo26v0cSVOSSAkhhBDN2Ow2Xtv0GgDXjboOU2QGpE7Q9tYrPag1Cov3Y4Tti92/gufyC1FPuMbfoXhEnSORCjI0DKZN7h+LtbqUuPAgf4UliZQQQgjR3IKsBewr20evsF6cPfBs0Bvh2sWw8AFY9W+KYicQlxLg5QRGnAOAMuxMPwfiGXVWGwBBhoYeqb/NGcwYQw5DkiP8FZYkUkIIIURjVruVVze/Cmi9UUa9Y5PfqiJY8xYAWX0vJM5fAbrhUPkhsowwZd7TmPQmf4fjEXWWlj1SgSCwohFCCCH8bP6B+WSVZ5EansqZAxv15qx+ESxVMOhkKiIH+S9AN3y19ytuWnwTz69/3t+heIxzaM/YkLocLa8lv9pOjdnmr7AkkRJCCCHqNe6NumH0DRh1jXqjfn9De37C3X6Kzn2LHNXMZ6ZM9XMknuNyaO+TTdy5vIaNh0r8FZYkUkIIIUS97/Z9x+GKw6RHpHN6/9MbTqx+SeuNGjgH0sb7L0A37D+4nCxbJVE2O+MTx/o7HI9xNdk8EARWNEIIIYSf1FpreXnTy4DWG2XQOaYRVxfDmm7UG7X5bQBmBiViCPLfJGxPkzlSQgghRAD7aNdHHK0+ypCYIZzW/7SGE6tfAnMlDJgNvSf6L0A3LS7aDMCc/vP8HIlnOYf2AqyOlCRSQgghjnlldWW8tVVbkXfr+FvRKY5/Hi01DXOjZgZ+b1TO4dXs0NkIsatMHXudv8PxqEAd2pPyB0IIIY55b255kwpzBZNTJjOt17SGE3Yb2Oq05yEx/gmuA/J3fEk/s4VBockEBUf5OxyPqu+RMgVYIhVY0QghhBA+llOZw0e7PgK03ihFabSXW1A4jL5Qe77qX74ProPG7FvFt0dyeWzc7f4OxeNkjpQQQggRgF7a+BIWu4VT+53K8LjhLRtM/xsoOtj8KZTn+jw+t9ltMPZS6H8iIYNO8Xc0HtcwtNcwR+q+04Zy58RghvWK9FdYkkgJIYQ4dm0r3MZ3+7/DqDPyl7F/cd0obgD0ngx2C+Tv8G2AHbC5aBtHRp4Fl38Nhp5RzbyxhjpSDanLiNQohsXpiQ713+eVREoIIcQxSVVVnlrzFACXDruU3hG9W2+sOHpB6reLCUCPrH6EuV/MZW3eWn+H4hWuKpsHgsCKRgghhPCRn7J+YlPBJmKDY7luZDsr3OwW7TFA963bt38xmSWZRBjCGJ0Q4Jspd1LDHKmGob0Pfz/Ip7vNZBVW+SssSaSEEEIce2qsNTy3/jkAZqfP5kDZAWz2NvZrszkSKV1g9kj9sOl1AE7SRfaYTYqbczW09/3mXOYfsJBTWuOvsCSREkIIcez56cBP5FXlAfB55udc/OPFfLzr49bfUJ9I6QOvapDdZuX7Um3u1ukZf/RzNN7jarJ5IJBESgghxDFnTOIYjks9joHRA53H+kf3b/0NATy0t3bzO+TqFXrZYPyoK/wdjtfIHCkhhBAiQPSL6serc14lIzYDgBN7n9i0EGdjRfu0HxQIifVdkG76dtcnAJwRMxxdAPaYeYqrob1AEFjRCCGEED6y/uh6vt//PSadiTsn3tl6w5/v13qkxlwCEUm+C9AddZXoSw8TZLdz5vib/R2NV7mabB4Iem7qKoQQQrTCarfy+O+PA3D1yKtJi0hz3XD/Mtj9A5jCYfb9vgvQXbt/5JH8fO5Kn0xY+nHasXdPh9zNrtuP+iOc9ixYaqF4H2T+BMd3jyrosteeEEIIESA+2/0ZmSWZpIan8qcRf3LdyGaFn+7Vnh9/G0Qk+y5AdxlDQdETdt57Dccs1VBX7rq91bG6bed3WpJYkduNEinH0F6jOVKJkUEkhCgEGf3XSyWJlBBCiGNKYU0hL216CYC/T/w7wYZg1w03vg/52yE6Habc5MMI3XO44jB7wkKZcfdBDKbwhhOXfwtqK6Uc6ss37PxW+5n7pPcD9QBVVTE7eqRM+oZE6t8XjmXZsjLG9/HfhtKB1T8mhBBCeNnTa56mwlzB9NTpzOo9y3WjmlJY8pj2/KRHwdhKsuVHH+/6mL8u/SvPbXkVmm+0HBzl+scUCpYa2LtIa5txun+C7yCzzY5dBaNewaAPrNQlsKIRQgghvGh59nLmZ80nxBDC/VPuR2mcgDRp+AxUF0H6NBh2lm+DdIPZZua7fd8BcFq/0zr25n1LteG/lDEQ3ca2OAGk1jHRPDjAJpqDJFJCCCGOEdWWah77TetlumnMTaSGp7puWJkPv2uVwpl6Y9PengCx+NBiSutKyYjNYFjcsI69eaeWgDG0e/RGAdRatKHKYFPTROrqd9fy50VV/L6/yB9hAZJICSGEOEa8uPFFcqtyGRY3jEuGXtJ6Q0WnDYMBfHENrPwXqKpPYnTXZ7s/A+DcQee23qvmitUMu3/Unmec4YXIvKPGrCVSIc0mlVebbdRYwWb33/cjiZQQQogeb1vhNj7a9RF6Rc9DUx/CoGtjrVVYPNy4GkZeANZaWPQg/PaK74Jtx+7i3aw7uo5QQyin9+9gr1LeVjBXQsJQSMzwToBeUGNxnUgFAlm1J4QQokez2Cw89OtD2FU7Vw6/kqFxQ9t/U3ginPsmDD4FvrgaFj4AaZOg90TvB9yOT3ZrlczPHHAm4Y1X67kjbTzcsQfKDnshMu+pH9rbk1/BmEd+dh6vrLVqj2arX+IC6ZESQgjRw72+5XV2l+wmNTyVP4/+c8fePPI8mHIj2K3wv6ugutg7QXbA4JjBpIWncdHQizp3gdBYSBnt2aC8LD02lOhQI3YVSqstzh+rY0jvYGGV32KTHikhhBA91rbCbby19S0UFB6b/hihxtCOX2TOw3DoN8jZAF/fCBd97PlAO+CijIu4cMiFHZsbBVoSGBwFusAbHmtPXHgQv987m+q6pvWxZjyzlIpa//VGgfRICSGE6KFqrbXct/I+bKqNy4ZdxoTkCZ27kMEE57+rJSGZ82H1yx6NszM6nEQB/HgHPDcU9i3xfEA+EGTQExNmavITCOspJZESQgjRI7208SX2l+2nX1Q//jL2L127WEwf+MOr2vNFDxJZtrvrAXbQ8uzlvLTxJQprCjv+5ppS2PWDVtohbqDHY/OXpEitUGpihP8KpkoiJYQQosdZ///t3Xd4VMXXwPHvzW56h1R6DxACIfSAVGkiSn1BUUFsoIK9NxRUxAKCKPpTRAQVsaGggCgRJHQINXRCCYEU0jZ1NzvvHxsWIgkkIcmmnM/z5Nly5849dweyJ3PnzlzYyeKDi9FpOt7s/mbRy8CURMshlqVizCZaH5xVoeOllFJ8uudTPt37KWti1pS8gv0/Wu5AbNzTsuRNNeHj5giAr7ujzWKQREoIIUS1kmHM4OV/X0ahuC/kPkJ8Q8qu8punQd0OOOUkwi+TK2x+qaiEKPYm7sXDwYPhzYaXooKllsf2d5VtYEISKSGEENXLm1ve5KzhLC1rtWRS20llW7neAUZ9iVHvCkdWw+aPyrb+IizavwiAMUFjSj5gPj4aYneCo0eVWVuvuFKzjJbHTKPNYpBESgghRLXx2/Hf+O3EbzjrnXnnpnew19mX/UG8G3Ko5WOW5+umwZltZX+MK8SkxrD+zHrs7ey5s9WdJa9g9xLLY5sRlkWLq5GzyZmWx5RMm8UgiZQQQohq4VTaKetaes93fp4mXk3K7VhJPl2g26OW+aWWl+/8Ugv3L0ShGNp0KD7OPiWv4Ox2y2OoXNYrD5JICSGEqPKMeUae3fAsmaZMBjUaVLpxRCXV7zWo2xHSzsLPk8BsLvNDxGfG89vx37DT7LivzX2lq+Te1TBxLdQr5fQP4pokkRJCCFHlzdk1h4NJB6nrVpdXu71aunmWSkrvAKO/tMwvdXRNuYyX8nX25dP+n/JY2GM08Cjl3XZ2dtCgC1TEZ1IDSSIlhBCiStsUu4nFBxcD4OPsw+ydszmYdLBiDu7VAIYtsDxfNw1Oby3T6jVNo3NgZya2mVjynbNTIe1cmcYjriaJlBBCiCrt79OXZ+rek7CH5UeW892h7yougJa3WMZLqTz4YWKZjZdKzk6+sQp2L4HZwRAxs0ziEYWTREoIIUSV9mj7R5nefTpdArsA4Kx3Znzw+IoN4uZpUK9TmY2Xis+MZ+CPA3ky4kmM5lLc2q8U7FoMygx+rW4oFnFtkkgJIYSo0rydvGnq2ZSdF3YCMK3bNJp6Na3YIHT2MGohOHnlj5ead0PVfbb3M7JMWQDY25ViCoeTGyDhELgFQNAtNxRLZdYywAOAoAB3m8UgiZQQQogqLTk7maf+eQqT2cTYoLHc0sRGiYNXAxh+abzU66UeL3Um/Qw/HvkRO82OR0IfKV0s2z6zPHacaEnyqimdnWUAvd7OdumMJFJCCCGqrDxzHi9sfIG4jDja+rTlmU7P2DagoMEQPiV/vFTp5pf6OOpjTMrErU1uLV3PWsppOPw72NlDhwkl31+UiCRSQgghqqzP9n7GpnObAAjxDeGXY79gyDXYNqh+r+WPl4qFnx8q0Xipo8lHWXViFXo7PZPbTS7d8bd/YRkbFTwc3P1LV0cVEZOUYXlMzLBZDJJICSGEqJKUUtZpDwCWRi9l+pbprDyx0oZRkT9e6sv88VJrIXJusXedu3suCsWo5qOo516vdMfPSQdNB50fLN3+VUhGjsnymGuyWQySSAkhhKiSNE3jje5vcE/re/B19gWgllMtetfvbdvAALzqXx4v9dcbcHrLdXcxmU34u/jjbu/OQ+0eKv2xb/0AnjokM5lXEEmkhBBCVFn9G/anZa2WJGQloNf0fND7AwJcA2wdlkWB8VITISPpmsX1dnpe7voyq0etLt2aeldy85OZzCuIJFJCCCGqrAOJB5gWOQ2wLFTcwb+DbQP6r36vQb3OJRov5eHgcePHNefBvh/g8B83Xpe4JkmkhBBCVEkJmQk8tv4xcs25jGoxiv8L+j9bh3S1S/NLOXvDsT8h8sOrimQYM5iwegJ/n/4bpVTZHDf6V/jxPljzIuTZbvxQTaC3dQBCCCFESWUaM3n070e5kHmB9n7tebHzixWzUHFpeNW3rMf37Rj4azrU7woNu1k3L9y/kJ0XdrLrwi7C64ajYTmPfg36MarFKACOJB9h9s7ZRR7itW6vFbyk2XIo1GoCF0/Avu8h9M7yOTchiZQQQoiqJc+cx/Mbn+dg0kHqudVjTp852Ff2SSeDBkH4VMsdfD9MhEn/gmttAA5fPAyAQrEpdpN1l8aeja3PU3NS+Tf23yKrzzRlFnxDp4eez8Ivk+CfWRDyf5b3qhkPZ3vSsk14ONmu/avfpyqEEKJae2/He6w/sx4PBw8+vvljajnVsnVIxdPvVTiz1fLz80Nw5/dgZ8ebPd5kb8JeFAUv69V1q2t93tyrOfP7zS+02kMXD2HMK2Q9vpDRsOFduHgc9i6D9uPK9HQqg/reLpxNzqJ+LRebxSCJlBBCiCrjm+hvWBK9BICJbSaSm5eLUqryXta70qXxUgt6WMZLbZoDNz2Jp6MnN9W76Zq7ejl50bNez6veV0rx0e6P+CTqE+b3m0943fArjqeHXs/Bzw/ChlnQ9v+q9XIxtiKDzYUQQlQJiVmJvLP9HevrObvmMOq3UXxz6BsbRlVCnvVg+KeW53/PgFORN1TdutPriL4YjbeTN+39219doM1IqN0MkmMsd/FVM7kmy12QOaY8m8UgiZQQQogqwdPRk4ENBxLiE1Lg/c/2fsYT65/gi31f2CiyEmoxELo/lj+/1H2QkViqavLMeczbPQ+Ah9o+hLPe+epCOj3c/DoMegeCh91A0JXT0fh0AI7F225ZIEmkhBBCVAn2dvbM6jWLb4Z8Q78G/azvX8y+yLrT6/h076cYzYWMFaqM+r4C9btA+rkSr8d3ycoTKzmZepK6bnUZ0XxE0QVb3QpdJ4F9IYmWuGGSSAkhhKhyXg9/nTm953BL41us773Q+QXs7arIGCDr/FK14Ng62FT01AaFMeYZ+WTPJwA8HPpw8e9azEqBwgami1KTREoIIUSV4+noibuDO3+e+hOAx8IeY3jz4TaOqoRuYLzUj0d/JNYQSxPPJgxpPKR4O+1cBHPawp5vSx6rKJIkUkIIIaqcwxcP89j6xzCajdzZ8k7ua3OfrUMqnRYDoPvjoMz56/EVb7xU58DODGg4gCntp6Cz0xXvWA5ukJNqmRLBlFv6mEUBkkgJIYSoUk6nnWbSukkYjAYGNhrIc52fqxrTHxSl78uW2c7T4+CnB4s1XqqJZxPe7/0+Nze8ufjHCR4OPi0g5bT0SpWhck2kNE0bpGnaYU3Tjmma9nwh28dpmrY3/ydS07R25RmPEEKIqu18xnnuX3s/iVmJdAnowls93sJOq+J9AleOlzr+1zXHS2WbsjGrkg9MB8BOZ5lXCmDDe9IrVUbK7V+fpmk6YD4wGGgN3KFpWuv/FDsJ9FJKtQWmA5+VVzxCCCGqtsSsRO5fez9xGXG0823H3L5zcdA52DqssuFZF0bkfwX+PQNiNhVa7L0d73H373dzLPlY6Y4TPBx8giD1NOypQvNvFaFhbVfLYy1Xm8VQnml8Z+CYUuqEUioX+A64/coCSqlIpVRy/sstQL1yjEcIIUQVlZSVxANrH+BU2ila1mrJxzd/jIu97ZYFKRfN+0OPJyzjpX68DwwJBTZHJ0Wz/MhyDiQduGo5mWKz00GvZy3Pq0GvlJujZYEWNyfbLdRSnolUXeDMFa/P5r9XlPuAP8oxHiGEEFVQYlYi9625j2Mpx2ji2YRP+3+Kh4OHrcMqH32uGC/18+XxUmZl5q2tb2FWZu5oeQfNvZuX/hjBw8G3Jbj5g+FCGQVec2lKlTKrvV7FmjYaGKiUuj//9d1AZ6XUlELK9gE+BnoopZIK2f4g8CCAv79/h++++65cYgYwGAy4ubmVW/2i+KQtKg9pi8qjprVFqimVuRfmEm+KJ9A+kEf9H8VDZ/skqjzbwTE7kQ47n8DBmMaJxndxuuFothq2siRpCe527rxS9xWc7W5sck373BSM9p5QlQfpA89vyOR8puKBEAe61y2/OcT69OmzUynVsbBt5dkXdhaof8XresC5/xbSNK0t8DkwuLAkCkAp9Rn546c6duyoevfuXebBXhIREUF51i+KT9qi8pC2qDxqUltcGlgeb4qnhXcL/jfgf9RyqmXrsIAKaIemXrB0JE1ivsGn++28tvN3AF4If4HBTQeX33GrmMyINYAJv/pN6N2zqU1iKM9Le9uB5pqmNdY0zQEYC/x6ZQFN0xoAPwF3K6WOlGMsQgghqpA4Qxz3rr6XU2mnaFWrFV8M+KLSJFEVovnN0ONJUGY+WPc4F7MvEuYXxq1Nbi3b4yQcgVVPgymnbOutQcqtR0opZdI07VFgDaADFiqlDmiaNil/+wLgVaA28HH+HCCmorrOhBBC1AyxhljuW3MfsYZYgmsH82n/T/F09LR1WBWvz0twejPNU/bh6ezDq11eLtv5spSCH+6FC/vBrxV0qqKTmtpYuU6+oZT6XSnVQinVVCn1Zv57C/KTKJRS9yulvJVSofk/kkQJIUQNlmHM4N7V9xJriAUgJSeFe9fca11XrkbR6WHUQsaZHFkbc4qm+38p2/o1DXo+bXm+8QPplSqlKj6LmRBCiOrkYtZF4jPjra9jDbEcTT5KxJkIm8VkK+m56eBRB0Z8hotSsP4tiPm3bA/S6nbwaw1pZ2H312Vbdw0hiZQQQohKo75HfdaNXsfH/T62Xs6r61aX93q9Z+PIKtbOCzsZ8MMAfjjyAzS7GW56Kn89vvvAEH/9CorLzu7ybOfSK1UqkkgJIYSoVDKMGby59U1Sc1Jp5tWMxYMXU9+9/vV3rCYyjBm8sukVDEYDFzLz53nq/SI0CAfDefjpATDnld0BW90GfsGQFgu7FpddvRXAQW9JY+x1tktnJJESQghRaexJ2MNdv99FrCGWEJ8Qvhz4JX4ufrYOq0K9ueVNzqSfIcg7iAdCHrC8qdPDqC/ApTaciLD0HpUVOzvond8rtWlu2SZp5ay5nzsALfzdbRaDJFJCCCEqhYgzEdy/5n5SclLoUbcHnw/4HC8nL1uHVaF+O/4bv534DWe9M7N6zSq4lmD+eCnQIOItOLmx7A7ccqhluoV7frEsIyOKzXaL0wghhBD5/jj5B89vfB6zMqPTdDjqHHk18tWrytV3r8+U9lOw06pfP8DptNPM2DIDgBc6v0ATzyZXF7o0Xmrje5b1+Cb9C25l0GNnZwc3v3bj9dRAkkgJIYSwud+O/4ZZWdaVy1N5/HX6r0LLaWjc2fJOfF18KzK8cqeU4pVNr5BpymRQo0EMazas6MK9X4DTm+HUJst4qbt+KtteJKUs46U865VdneXk0Pk0AKLj0ghv5mOTGCSREkIIYXMzesxg2/lt8J/lX41mI/N2zyMuIw47zY6XurxU7ZIoAE3TeKnrS8zZOYdXur1y7Yk3dXoY+QUs6JE/Xup96PVs2QRiSIBvRkP6eZgaBfZOZVNvOckzW/7BmMtp3eDikERKCCGEzdVyqsWgRoMKvJeak8oTEU8QlxGHs96Z93q9R896PW0UYflr4d2Cj2/+uHiFPQIt46WWjISIt6FBV2hcBp+Nqw+YTZAeZ7mDr8uDN15nNVf9LjILIYSo8s6mn+XuP+5m+/nteDt688nNn9A5oLOtwypz0UnR/Hjkx9Lt3KyfZWZyZYYf7y+b+aU0DXo9b3n+7wdgzL7xOqs5SaSEEEJUKsY8IxPXTORk6kkAknOSmbB6Ap2XduarA1/ZOLqyczH7Io+tf4xpm6ex8sTK0lXS63lo2AMMFyzJVFlMXdByCASE5PdKVZ/Pu7xIIiWEEKJSsdPsCHQNxFnvjKPO0fq+QmFvZ2/DyMqO0WzkqYiniMuIo61vWwY0HFC6inR6GPk5uPjAyX9gQxnMAK9plgHtYJmvyph143VWY5JICSGEqFR0djq+GvwV60avo2OAZS17vabn5S4vc2erO20cXdl4d/u77LiwA19nX2b3nl1wvqiSujReCs0yXurEPzceYNAtENDWMpP6TumVuhZJpIQQQlQ6J1NPMm7VODbFbsLL0YvPBnzGmJZjbB1WmVh8YDHfHvoWezt7ZveZXTYzt18aL4WyXOJLv3Bj9V3qlXLyqtQTdPp7WO4q9HO33d2FkkgJIYSoVP6N/Zdxq8YRkxZDc+/mfDvkWzoFdLJ1WGViTcwa3t3xLgCvh79OO992ZVd57xeg0U2QEQ8/lcF4qaDB8Pg+6PxA2cRXDnzcLJd+fd0dr1Oy/EgiJYQQolIwKzOf7PmEh9c9TLoxnX4N+rFk8BLquVf+iSGLK7h2MA09GvJUh6cY2nRo2VZup7OMl3L1hZMbYMO7N1afpoGTR9nEVo1JIiWEEKJSeH3z63wc9TEqf1bOfYn7uH3F7Ty49kGMZqONoysb9dzr8f2t3zOhzYTyOYB7wBXjpWaWzXip3EzYPB92fX3jdZWx1CzLv4vUTNv9+5BESgghRKUQa4gt8Do+M57zGefZn7SfbFPVnc/odNppvj74NSp/9m0Xe5fyPWDTvtDzGcpsvFTMRljzIvw9vdLdwXc2OdPymJJpsxgkkRJCCFEpfHrzp6wduZZJ7SahYVkipalnUxYPWoy7g7uNoyudc4ZzPPjng8zaPovlR5ZX3IF7P19246WaD4DAUMtcVTsWllmI1YUkUkIIISqF7LxsPtz9IQv2LEChGNZsGN8M+YZm3s1sHVqpnE0/y72r7yXWEEub2m0Y0mRIxR38v+Ol/plV+rqunFfq3zmWS33CShIpIYQQNnf44mHGrhzLqhOrcNY781aPt5jefXr5XwYrJ2fSzjBxzUTOZZyjrU9bPh3wKa72rhUbhHsAjPgfoME/71gWOC6tFgOhTntLD5f0ShUgixYLIYSwmTxzHl8f/Jq5u+diNBtp5tWM93u/TxPPJrYO7bqMRiNnz54lO7vg+C2T2URSVhLPNXwOB50DtZxqEXs8llhii6ipPAXA7ashOxXiTZC1v/TzQnX9ADISQNPBwQOg2b4vZvZAX8wKPJ0ziI6OvuH6nJycqFevHvb2xZ9BXxIpIYQQNhFriOWlf19i54WdAIxqMYpnOz2Ls97ZxpEVz9mzZ3F3d6dRo0ZommZ9PyY1BjujHS72LjRwb4DO1hNaKgVJxyDXAA6OULuZ5XJdaepJPALGTPD0B1efso+1hMznUskzKwI9nfC9wUk5lVIkJSVx9uxZGjduXOz9JJESQghRoZRSrDi+gpnbZpJhzKC2U21eD3+dXvV72Tq0EsnOzr4qiQKo61aX+Kx4AlwCbJ9EgSVp8m4ECYcsyVT6ecuyMqWpx6MumI2WGc+rGU3TqF27NgkJCSXaTxIpIYQQFeZ8xnmmb5nOhrMbAOjXoB+vdnuVWk61bBxZ6VxKorJN2TjqHNE0DXudPXXd6to4sv/Q2VuSqaRjlvXzHFxLN9mmo1uZh3YjnPQ6MnJNONmXTcL636S4OGx/gVMIIUS1Z1Zmvj/8PcNWDGPD2Q2427szo/sMZveeXWWTqEuSs5M5kXKChKyS9WTcqKSkJEJDQwkNDSUgIIC6detaX+fm5l69g6M7uAUAsCPid6ZOebREx2vUqBEhISGEhoYSEhLCip9+uPFlaK4wbdo03nvvPQBeffVV1q1bd919LuU9pbhQWWakR0oIIUS5Op12mmmbp7H9/HYA+tTvw8tdXy6bxXptLD4znoRMSwJlVmaUUqXq1SiN2rVrExUVBViSEDc3N55++mnrdpPJhF7/n6959wDINdCxbRAdO3awjHsqQbzr16/Hx8eHw7siGXDbSG4fcBO4+ZfF6RTwxhtvlHmd5UV6pIQQQpSLnLwcFuxZwIhfR7D9/HZqOdXi3Z7v8mGfD6t8EmVSJpKzk61JVKBrIAGuARWWRBVlwoQJPPnkk/Tp04fnnnuObdu2ER4eTvv27QkPD+fwkSPg3YiILbu5dexESD/PtGnTmDhxIr1796ZJkybMnTv3usdJy8zB29MDDPFgzmPYsGF06NCB4OBgPvvsMwDy8vKYMGECbdq0ISQkhNmzZwNw/PhxBg0aRIcOHbjppps4dOhQoefxww8/AJaesNdee42wsDBCQkKs5TMyMnh6ymTuHNKX8C6dWLFiRVl9jCUiPVJCCCHK3IazG5i5bSZn0s8AcGuTW3m207N4O3nbOLIbl5qTyscXPuYRt0fw1ryp516PkFc3lMuxYmaWfBLPI0eOsG7dOnQ6HWlpaWzYsAG9Xs+6det48cUX+fHHH62X+DCcB1MOhw4dYv369aSnpxMUFMTkyZMLnQKgT58+KKU4ceIE3//vAzCbICORhQsXUqtWLbKysujUqRMjR44kJiaG2NhY9u/fD0BKSgoADz74IAsWLKB58+Zs3bqVhx9+mL///vua5+Tj48OuXbv4+OOPee+99/j8889588036dz9Jqa9Nw8Xlc0t/Xpy88034+pasfN1SSIlhBCizMQaYnln2zusP7MesCzx8lLXl+gU0MnGkZWdd7e/y9Gco+g0HY08G1W66RpGjx6NTmcZfJ2amsr48eM5evQomqZhNOYv7uvgAvr86QKyUxgyeBCOjo44Ojri5+fHhQsXqFev3lV1X7q0d/z4cfr17Uvvdd/gZhfP3E+W8/Mvlh6hM2fOcPToUYKCgjhx4gRTpkxhyJAhDBgwAIPBQGRkJKNHj7bWmZOTc91zGjFiBAAdOnTgp59+AmDt2rWkGjJZtGAeep0d2dnZnD59mlatWpX6sysNSaSEEELcsJy8HL7c/yWf7/ucnLwcXPQuPBz6MHe2uhN7u+JPblgVPN7hcY6dO4aPs481iSpNz1F5ubJH5pVXXqFPnz78/PPPxMTE0Lt378sF9Y7g4AbKjKM50zpeSqfTYTKZrnmMpk2b4h8QwMETcWSmH2Td2jVs3rwZFxcXevfuTXZ2Nt7e3uzZs4c1a9Ywf/58vv/+e+bMmYOXl5d1bFdxOTo6AhSITSnF7M8W06BJszKZR6q0ZIyUEEKIUlNK8c+Zfxi+Yjjzo+aTk5fDLY1v4bfhvzE+eHy1SKIyjZl8vu9z8vLvUPNx9mGS36TKMUfUdaSmplK3rmUqhkWLFl1dwLuRZYbyvBzL/FLFFB8fz8mTJ2nYMpTUdAPebk64ODly6NAhtmzZAkBiYiJms5mRI0cyffp0du3ahYeHB40bN2b5cssCzkop9uzZU6pzGzhwIN98+RlKKQB2795dqnpulPRICSGEKJUDSQd4f8f71rvxmnk148UuL1ary3jHko/x5D9PcjL1JLl5uTwc+rCtQyqRZ599lvHjx/PBBx/Qt2/fqwvo7PMn18y+PL/UNfTp0wedTofRaGTmzJn4N2jKoP79WbD0F9qGhhIU1JKuXbsCEBsby7333ovZbAbg7bffBmDp0qVMnjyZGTNmYDQaGTt2LO3atSvxub3yyiuMf+BhRvXvjt5Oo2mTxqxcubLE9dwo7VImV1V07NhR7dixo9zqj4iIKNj1KWxG2qLykLaoPCpDW8QaYpm3ex6rTqwCwMPBg0ntJjG25dhq0QN1yW/Hf2P6lulkmbJo6tmUD3p/QBMvyxqAERER+Pv7V/h4nHKTHmfpkbLTg28Q6ByKv6/ZDHa2ucB1oAyXiLkkOjr6qnbVNG2nUqpjYeWlR0oIIUSxpOak8sW+L1gavZRccy72dvaMazWO+0Pux9PR09bhlZnUnFTe2fYOv534DYChTYbycteXcbF3sXFk5cgtAHIMliVkkk+VbD0+GyVRADpNIw+Fzs52005IIiWEEOKaMo2ZLIlewqL9i0g3pgNwS+NbmBo2tfIthXKDzqaf5e4/7iYxKxFHnSPPd36ekc1H2nx+qHJ31Xp8ceBRp2R15BggIx68GkIFjR9z0NuRm2fGQWe7ZE4SKSGEEIXKNGby/eHv+fLAl1zMvghA18CuPB72OME+wTaOrnzUcatDE88mNHBvwOvhr9PIs5GtQ6o4Bdbju2C5o6+46/EpBennIDcDMhIsM6jXEJJICSGEKCDTmMl3h7/jqwNfWROotr5tmdp+Kl0Cu9g4urK3JmYNwbWDqedeDzvNjg96f4C7gzt2Wg28sd3RHdwDLT1SKaeKP15K0yz7JR2zzHbu6lshvVJmVfDRFiSREkIIAUCGMYNvD33L4gOLSc5JBiDEJ4RJ7SZxU92bqt3lrcSsRN7a+hZ/nvqTLgFd+N+A/6FpWrUa71Uqbv7546XSSzZeysHNctdfBfZK5Zjyrni0zY0OkkgJIUQNdzH7IssOLWPpoaWk5qQC0M63HZPbTSa8Tni1S6DMysyvx3/lvR3vkZqTiovehQGNBtg6rMpD08C7YcnHS9moV8rWJJESQoga6mTqSb4++DW/Hv+VnDzLMh3t/dozqd0kugV2q3YJFMCO8zuYtX0W0RejAegW2I1p4dOo41bCgdWVQFJSEv369QPg/Pnz6HQ6fH19Adi2bRsODte+JBcREYGDgwPh4eFXbVv09VKeeeZp6vrVxmgy0ap1GxYv/RYXl+vcuejgZvnJNVy3V8rNzQ2DwcC5c+eYOnWqdZHiqkYSKSGEqEGUUuyK38WiA4v458w/KCyDS3rV68X44PF09O9YLRMogOTsZCatm0ROXg5+Ln48HvY4tza5tcqeb+3ata1LrUybNg03NzeefvrpYu8fERGBm5tboYkUwJgxY/no7ZchPY47H32JZd8u5d77Hrh2pZpmSZ6SjkFGouUy4XU+3zp16lTZJApkiRghhKgRTGYTq2NWc+eqO5mwegIRZyKwt7NnZPORrLh9BR/1+4hOAZ2qbFJRlLTcNMzKMrO2t5M3D7V9iIdDH+a3Yb8xtOnQane+O3fupFevXnTo0IGBAwcSFxcHwNy5c2ndujVt27Zl7NixxMTEsGDBAmbPnk1oaCgbN24svEI3f0x2zmRkZOJtbwSl+O233+jSpQvt27fn5ptv5sKFCwD8888/hIaGEtrlJtoPvod0pzqgabz77rt06tSJtm3b8tprr111iJiYGNq0aQNYlrEZMWIEgwYNonnz5jz77LPWcmvXrqVbt26EhYUxevRoDAZDGX96pSM9UkIIUY1lGDP4+ejPLIleQqwhFgAvRy/GBI1hbMux+Dj72DjC8mEym1h+ZDkfR33MUx2fYlizYQA80PY6PSo3oNHzq4rc9tbwEO7s0gCAb7ae5sWf9xVZtrQLICulmDJlCitWrMDX15dly5bx0ksvsXDhQmbOnMnJkydxdHQkJSUFLy8vJk2adM1erGXLlvHvv/8SFxdHi0b1GNqnM6TH0aNHD7Zs2YKmaXz++efMmjWL999/n/fee4/58+fTvXt3DAYDTk5OrF27lqNHj7Jt2zaUUtx2221s2LCBnj17FnkeUVFR7N69G0dHR4KCgpgyZQrOzs7MmDGDdevW4erqyjvvvMMHH3zAyPsfK9VnVZYkkRJCiGooOima5UeWs+rEKjJNmQA0cG/APa3v4bZmt+Gsd7ZxhOVDKcXG2I28v+N9TqSeACAyNtKaSFVnOTk57N+/n/79+wOQl5dHYGAgAG3btmXcuHEMGzaMYcOGFau+MWPG8NFHH6GU4pFJD/DuJ4t5/tF7OXsumzEvvkZcXBy5ubk0btwYgO7du/Pkk08ybtw4RowYQb169Vi7Zg1r166hffv2ABgMBo4ePXrNRKpfv354elrunGzdujWnTp0iJSWFgwcP0r17dwByc3Pp1q1bqT6nsiaJlBBCVBOZxkxWx6xm+eHl7E/ab30/zC+Me4LvoXe93uiq6V1UZmUm4kwEn+/7nH2Jlt6e+u71earDU/RtUMhiveWguD1Jd3ZpYO2dKktKKYKDg9m8efNV21atWsWGDRv49ddfmT59OgcOHCh2vZqmMXTYSObNfo/ngSlTp/Lks89z27CRREREMG3aNACef/55hgwZwu+//07Xrl1Zt+YPVEYSLzx8Dw89/ZplHb9icHR0tD7X6XSYTCaUUvTv359vv/22QNmjF9LJMufhoLfdv2tJpIQQooo7fPEwy48sZ+WJlWQYMwBwd3Dntqa3Mar5KJp5N7NxhOVv3al1PPXPUwB4O3pzX8h93NHyDhxKsvhuFefo6EhCQgKbN2+mW7duGI1Gjhw5QqtWrThz5gx9+vShR48efPPNNxgMBtzd3UlLSytW3f/++y9NW7QCB3dS09Kp666BUnz11VfWMsePHyckJISQkBA2b97MoaPHGdivF6+8/QHj7h6PW2AzYmNjsbe3x8/Pr0Tn1rVrVx555BGOHTtGs2bNyMzM5OzZs+i9LXdb6mw41E0SKSGEqIIyjZmsiVnDD0d+YG/iXuv7ob6hjA4azYCGA3DSO9kwwvKVm5fLwaSDhPqFAtCnQR/C/MLo37A/I1uMrLaXLq/Fzs6OH374galTp5KamorJZOLxxx+nRYsW3HXXXaSmpqKU4oknnsDLy4uhQ4cyatQoVqxYwbx587jpppsK1HdpjJTZbKZevXosWrQIvL2Z9vTDjJ44lbp136Fr956cPHkSgDlz5rB+/Xp0Oh2tW7dm8ODBOJJLdPRBuvUdDHpH3NzcWbJkSYkTKV9fXxYtWsQdd9xBTo5lqo4ZM2bQppvtp63QlLLhvOql0LFjR7Vjx45yqz8iIoLevXuXW/2i+KQtKg9pi8pBKcXitYs563mWVSdWWRcQdrd3Z2jToYxqMYrm3s1tHGX5yjRm8uPRH1l0YBFpOWmsGbWGWk61KjyOiIgI/P39adWqVYUf2+Zy0i3TGwDUanr99fgSj1rmlXIPsEzYWYYOn08nx5RHfW8XvF3LpvcxOjr6qnbVNG2nUqpjYeWlR0oIISq5WEMsK4+vZOWJlcSkxcB5y/vtfNsxqsUoBjYaWO17YNJy0/ju0HcsObjEunxNM69mxGfG2ySRqtH+ux6fTxDor5HEuAdC0lEwJOTPdl52qYfJbC7waAuSSAkhRCWUmpPK2lNrWXl8Jbvid1nfd7dz5/ag2xnWbBhBtYJsGGHFMCsz725/l1+O/YLBaJk3KMQnhAdCHqBX/V41c2HhysDN39LLlJMOKTFQu3nRE286XjHbuSEBPMq2V8rWJJESQohKIsuUxb+x//L7id/55+w/GM1GAJx0TvRt0JehTYeScySHfp372TjS8pVlysJJ54SmadhpdpxIPYHBaKBLQBfub3s/XQK6VLuJNKscTQOvhpBw2LJI8fXW43MPtPRe6R2LLlNFSSIlhBA2lGXKYuPZjaw9tZYNZzeQZcoCQEOja2BXhjYdSr8G/XC1dwUg4miEDaMtX4cvHubHoz+y8vhKPur3EWH+YQA8FvYYT3R4gpa1Wto4QlGAzh68G+Vftrtg6XUqaryUoxv4tb7ucjFVkSRSQghRwTKNmWyM3cjamLVsjN1oTZ4A2tRuw8BGAxnceDD+rv42jLJiFHX34Za4LdZEqnXt1rYKT1yPo9vl8VLJMeDbsujxUtUwiQJJpIQQokKk56azKXYTa0+t5d/YfwskTyE+IQxoOID+jfpT162uDaOsWPN2z+Ob6G+sY5/c7d25temtjGw+skaM/6o2rhwvlRwDPtcYL5VnBEM82Oksd/FVA5JICSFEOYk1xBJxJoKIMxHsOL8DkzJZt7X1bWtJnhr2p46b7efCqQiJWYk46BzwcLBc/jGajRiMhhp192FZSkpKol8/y3i58+fPo9Pp8PX1BWDbtm04OBR9J92OHTtYvHgxc+fOLdaxPvzwQ06ePMmcOXMAeOihhzh+/Djr1q0DTWPekpUc3beduW88DennwKPgHwQLFizAxcWFe8aMgIx40OzAxQd0l9OQRYsWsWPHDj766KOrjv/WW2/x4osvXvW+hgaoQsfMXau+siSJlBBClBGzMrM/cb8leTobwdHko9ZtdpodHfw70Ld+X/o37E+gW/W6c6koFzIu8Nfpv/jz1J/sit/Fs52eZVyrcQCMazmOW5vcSgvvFjaOsmqqXbs2UVFRAEybNu2qBYhNJhN6feFf8x07dqRjx0KnRSpUeHg4S5cutb6OiorCbDaTl5eHTqcjcstWhg0ZZNloiM8fL+VpLT9p0qTLlTm4Q266JaG61gD1KxSVSDnZ22HIMeOkt93dm5JICSHEDcgwZrAtbhv/nP2HiDMRJGUnWbe52rvSvU53etfvzU11b8LLyctmcVakc4Zz/HnqT9adWkdUQpT1fb2dnoTMBOtrf1f/GjEOrCJNmDCBWrVqsXv3bsLCwhgzZgyPP/44WVlZODs78+WXXxIUFERERATvvfceK1euZNq0aZw+fZoTJ05w+vRpHn/8caZOnVqg3vbt23PkyBGysrLIzc3FxcWFZs2asW/fPkJDQ4mMjGTWrFkcT4jlkcefJCEpBRePWvzv889p2bJlgURv++FY7nvgflxdXOjR62b+WLOG/fsta0OeO3eOQYMGcfz4cYYPH86sWbN4/vnnycrKIjQ0lODgYJYuXcqSJUuYO3cuhsxsWoeG8cWnCwB7vvzyS95++20CAwNp0aJFgXX7yoskUkIIUQJmZSY6KZrIc5FEnoskKiEKk/nyJbtA10B61+9N7/q96eTfCXudvQ2jrThKXb688ubWN9lwdgMAjjpHutfpTv9G/elVrxfuDu62DLP8TPO8fplS1Zta4l2OHDnCunXr0Ol0pKWlsWHDBvR6PevWrePFF1/kxx9/vGqfQ4cOsX79etLT0wkKCmLy5MnY21/+t6vX6wkNDWX79u1kZWXRpUsXmjdvTmRkJH5+fiilqF+/Pv0mTGDBO2/QvF5ttu49xsMPP8zff/9d4Fj3PvQIn73/JuHtmvP8u18U2BYVFcXu3btxdHQkKCiIKVOmMHPmTD766CNr71t0dDTLli1j06ZNnEnJ4YWnHmPZd98w9JbBvPbaa+zcuRNPT0/69OlD+/btS/z5lZQkUkIIcR0XMi6wOW4zkbGRbI7bTEpOinWbnWZHW9+29Kzbk971e9PCu0WNmePoRMoJ1p5ay7pT63im0zN0CewCwJDGQ3DSOdG/UX961u2Ji72LjSOtWUaPHo1OpwMgNTWV8ePHc/ToUTRNw2g0FrrPkCFDcHR0xNHRET8/Py5cuEC9evUKlOnevTuRkZFkZWXRrVs3mjdvzltvvYWvry/h4eEYDAYiIyMZ/eBTYMoBpcgxFZxxPCUlhfT0dML7DYHEI9x5ay9W/hlh3d6vXz88PS1JaevWrTl16hT169cvUMdff/3Fzp076dSpE9nGPLKzs2lQN5CtW7fSu3dv6zixMWPGcOTIkRv6LItDEikhhPiPTGMmUfFRbDq3ichzkRxLOVZge6BrIOF1wuletzudAzrj6VhOvRGVjFKKI8lH+PPUn/x56k9OpJ6wblt/Zr01kbqlyS3c0uQWW4VpG6XoOSovrq6u1uevvPIKffr04eeffyYmJqbINTOvvASm0+kwmUxXlQkPD+fTTz8lOzubRx55BF9fXw4ePIivry/du3fHbDbj5eVFVNQeyDFY5pcCyL782VjX93VwtSw1o8xgzitRHEopxo8fz9tvv82Bc6nkmRWBnk5s+mu1Tf6IkURKCFHjZRoziUqIYsf5HWw/v539ifsL3GHnrHemU0AnwuuEE14nnEYejWpMr9OV7l97P9vOb7O+9nDwoG8Dy+D5roFdbRiZKEpqaip161ruoFu0aNEN1RUeHs69995L3bp18fPzA8DX15cVK1awfPlyPDw8aNy4McuXL2f06NEotwD2bt1AuzY6a7Lk7e2Nu7s7W7ZsoWuHdny3doFlKoTrsLe3x2g0Ym9vT79+/bj99tt54oknAEdSk5MxpebSpUsXHnvsMZKSkvDw8GD58uW0a9fuhs65OCSREkLUOJd6nLZf2M7289s5kHigQOJkp9nRqlYrutXpRvc63Qn1C8VBVzYry1d2SVlJbD+/nS1xW9h2fhuf9f+Meu6WSzzNvZtzMvUkvev35uaGN9MpoBP2djVjDFhV9eyzzzJ+/Hg++OAD+vbte0N1eXt74+vrS3BwsPW9bt26sWnTJmvCsnTpUiZPnsyMGTMwGo2MvW0A7YJbQFYKuFumvfjiiy944IEHcHV1pXfv3tZLedfy4IMP0rZtW8LCwli6dCkzZsxgwIABZOYa0evteX/2h4QFBzFt2jS6detGYGAgYWFh5OXlXbfuG6VZu9mqiI4dO6odO3aUW/0RERFFdn2KiiVtUXlU9bbIMGawJ37PdROnTgGd6BTQifZ+7SvtoOiybgtjnpHIc5FsidvC1vNbC0zZAPBG+BsMbz4cKLgGXk0XERGBv78/rVq1snUolVueCRIOgdkIbn7gUReDwYCbmxsAM2fOJC4ujg/ffRt0DgXmlSqOKy/t+bo7lUnI0dHRV7Wrpmk7lVKFzhchPVJCiGpFKcWZ9DPsSdhDVHwUexL2cDTlKGZ1edCrnWZHm9pt6BTQiY4BHSt14lTWcvJyiEmNsc4cnqfyeDLiSXLNuYBlgeT2fu3pHNiZroFdaVXr8heKTJYpSkynv2I9Psv8UqtWrebtt9/GZDLRsGFDFn30DiQetsyQXsx5pSoTSaSEEFValimLA4kHiEqwJE17E/ZyMftigTJ6TU+wTzAdAzrS0b8jYX5huDm42SjiimUym4hOimbr+a1sidtCVHwU9nb2bBy7Eb2dHie9E6ODRuNq70rXwK60821XYy5jigri6AbudSwzniefYszI4YwZM+by9twMSDwCGQng6lfiXilbq1rRCiFqvDhDHFEJUdbepsMXDxe4TAfgYOdAsE8w7Xzb0c63HcG1g3HUX74bKNece1WyVZnUcqp1w3UcuniIebvnsfvCbtKN6QW2NfRoSGJWIgGulrXOnu/8/A0fT4hrcvPLX48vLX89vmaWZWIg/w4+D8u2Esx2DmBvZ0eeOQ97ncxsLoQQ17XyxEpe2PjCdcvlmnPZHb+b3fG7KyCqsndXq7t4rvNz1y13MfsiB5MOciDxAAeTDtKqdismtbMsxaHTdNZJMeu716dLYBe6BHahc0DnMknUhCgRTQOvhpbxUsYMSIsDzyvW43MPyE+kEsDVF4o5ka1ep4EJ9Ha2G7cniZQQosrwdvQmwDWAHFOOrUMpMZMykZ6bfv2CcM1FjLcZtvFrxK8cSDzAuYxzBbYl5yRbE6nGno2Z1XMW7Xzb1ZhFkUUld+V4qYx4yyW/S+vxXdUrVfeaVVUmkkgJIaqM7nW78+eoP20dxnXl5uVyLOUY0UnRHEw6SPTFaI4kFz7DcmPPxoT4hNDGpw0hPiG08G5Bbl6u9e7CA0kHmNp+KvU9LLM7x+TEsDFpI2AZ/N2qVita125NsE8wbWq3sdart9MzuPHg8j9ZIUriP+Ol8G0J+vwxedZeqcT8sVLX75XKM6sCj7YgiZQQQpSho8lHGfHriGKXP5l6kpOpJ/n1+K9Fllkds7rQ97NMWeyK38Wu+F0ljvN6fr7tZ5p5NyvzekXZSUpKol+/fgCcP38enU5nXR5l27ZtODhc+6aBiIgIHBwcCA8PL/C+UgpfX1+OHj2Kt7c3cXFx1KlTh40bN9KjRw/AMhHnoUOHqF27dqF1h4eHExkZWfiB88dLNQq9iR1//oRPUGfLeCkHV3DyJCJyBw6eqYTf1PO6n0Funtn62KhRI3bs2IGPj8919ytLthudJYQQotKy0+TrobKrXbs2UVFRREVFMWnSJJ544gnr6+slUWBJpApLdjRNo0uXLmzevBmAyMhI2rdvby17+PBhfHx8ikyiLu1TpEvjpdDAlGkZL3WJd2Midhwkcuu2InevbKRHSgghylAzr2bsG78PgMjYSH4+9jOn0k4RkxZDlinLWs7N3o1Nd2yyJiyf7vmUWs61CK4dTHOv5tgXcVmjOBNyZhozOZ5ynCPJRwr8pOWmXVXWUedIUK0gWtVqRXDtYFrVbkVTz6ZFHl9Ubjt37uTJJ5/EYDDg4+PDokWLCAwMZO7cuSxYsAC9Xk/r1q2ZOXMmCxYsQKfTsWTJEubNm8dNN91krefSAsW33HILkZGRPPnkk/z444+AJUm61Iv17rvv8v3335OTk8Pw4cN5/fXXAXBzc8NgMGA2m3n00Uf5559/aNy4MWazmYkTJzJq1Ciw0zNv4Xf89udGjMqO5T/8iJOT01VxtWzZkkmTJnH69GkA5syZQ/fu3UlKSuKBO0ZzMSmRzp07Y6sJxiWREkKIEsoz53Eu4xwxqTHEpMUUeHysw2Pc1vQ2AM5lnCtwWa6WUy0aeTSikWcjWtVqhclsss7Z9FC7h8oktmf/eZY/Yv4odnkHOwdOp53mdNpp1sSsKdGxFIrUnKsX623k0Yhlty7Dxd6lRPVVedOusdTJrXOg472W5zu+hJWPX6Oe0i2ArJRiypQprFixAl9fX5YtW8ZLL73EwoULmTlzJidPnsTR0ZGUlBS8vLyYNGkSbm5uPP3001fVFR4ezhtvvAFYLhO+/vrrzJkzB7AkUt27d2ft2rUcPXqUbdu2oZTitttuY8OGDfTsefmS3E8//URMTAz79u0jPj6eVq1aMXHiRMtGTcOnTkN2rZnMx1/9wHuz3uHzhV9a4nK04+n7x0CtJtx593ieeOIJevTowenTpxk4cCDR0dG8/vrrhHXuyoOPPcv+zev5+ssvSvW53ShJpIQQogipOamcTD1JcnYyfRr0ASxfVj2X9Sy0dwcgJjXG+rxLYBdmdJ9BI89GNPJohKfj9dcUu1Fn0s+UqPx/55gqCzFpMRjNxjKvV1xbTk4O+/fvp3///gDk5eURGBgIQNu2bRk3bhzDhg1j2LBh162rc+fO7N69m4yMDIxGI25ubjRp0oRjx44RGRnJU089xeeff87atWtp3749AAaDgaNHjxZIpP79919Gjx6NnZ0dAQEB9OnTp8BxRowdD445dAgJ4qc/1sOlFQhys8CYCYZ41q1bx8GDB637pKWlkZ6ezoYNG5j5yVcA9B80GG9v71J/djdCEikhhACOJR9jQ+yGAr1LyTnJALjoXdhy5xY0TUPTNAJdA3HSO9HYo7E1Sbr0GOgaaK2zvnt96rvXr9DzWHLLEpJzktFpuhLva1Zmzmec50TqCU6mnuRE6gnr82tp5tWM5t7NaeHdguZezeng36HGzBxfQHF7kjree7l3qgwppQgODraObbrSqlWr2LBhA7/++ivTp0/nwIED16zLxcWFZs2asXDhQsLCwgDo2rUrv//+O/Hx8QQFBaGU4oUXXuChh4ruTb3e5TZHJyfwCkCn34HJlHt5vJRj/pJNGYmYzWY2b96Ms/PVSxRVhmUfJZESQlR7SimSspOuuhQ3vNlw+jW03PW0N3Evs3fOLrCfs96Zhh4NaeTRiCxTlvVS1bdDvq20Y4h0djp8nEt215LJbOKOVXdw6OKhEu3nau+Kr7OvdRma6KToEu1flWVmZvKq+6vok2/8a1ShMOZd7sFzsXehoUfDEg/4d3R0JCEhgc2bN9OtWzeMRiNHjhyhVatWnDlzhj59+tCjRw+++eYbDAYD7u7upKUV3rMKlnFSc+bMYdq0aQB069aNu+66i65du6JpGgMHDuSVV15h3LhxuLm5ERsbi729PX5+ftY6evTowVdffcX48eNJSEggIiKCO++8s+CBdHrwzJ/rLCMedyc9aVk54OgJOakM6NODjz76iGeeeQaAqKgoQkND6dmzJ6t+Ws79U5/mr7VrSE5OLtHnVVYkkRJCVGlmZeZi9kUuZF4gPiMeg9HA0KZDrdvHrRrHkeQjZOdlX7VvU6+m1kQqxCeEO1veae1ZauzZGD8Xv0K/zCprElVa2absEidRABnGDDKMGeUQUdVgMpvIzcst83ozjZmkZKegtyv+V3SOKQe9Wc+ibxbx9JNPk5aWhslkYvKjkwloGMAdd95BWloaSikmT5mMnbMdfQb24Z477uHnX35m1gez6Ny9M856Z+vi1N27d+fDDz+kW7duAISFhXH27Fnuv/9+AAYMGEB0dLR1u5ubG0uWLCmQSI0cOZK//vqLNm3a0KJFC7p06YKnZyGXuO2dIX+84NAebRk1+SVW/PIz86Y9xtzXHueRN+bTtm1bTCYTPXv2ZMGCBbz22mvcPvL/GDO4F31796ZBgwal+rxvlGarUe6l1bFjR7Vjx45yq784d8SIiiFtUXnYqi1MZhOJWYmczzhPHbc6+LlYfkH/dfovFh9YzIXMC1zIvIDJfHmtPQc7B3bctQMtv89/1K+jOJx8GHd796suw7Wu3Zp67vUq/LxuRHm1RXpuOsdTjuPh6FHmdVdH27ZtI7hOMC2CWpR4XzNmTqScKIeoykYTrybWZKosGAwG3NzcSEpKonPnzmzatImAgICrCyoFF09YJuW0dwGf5nAxBnJSLcvGeF79f/VEggFDjokmPq64OZXNHzjR0dG0atWqwHuapu1USnUsrLz0SAkhbCInL4e0nDR8XSwTCCqleGf7O1zIsCRHFzIukJidiDl/8OlLXV5ibMuxgKUn5MpJKL0cvfB38cfPxQ9/V39yzbk46iyLFH/U7yPc7N1q5pidEnB3cCfUL9TWYVQZp+1Po7fTF1gMu7iUUvi6+JKQmQBYLiGXpPfpRuXk5RTZk+agc8DB7vpzUJXErbfeSkpKCrm5ubzyyiuFJ1Hwn/X48ueXcg+wJFLZqZbFjCvh/GaSSAkhypxSytojtCdhD1vjtl5OkPKTpOScZOq51eOPkZZb9TVNY9WJVaTkpFjr0dDwcfbB38UfV3tX6/vdAruxcOBCa/LkpHcqMpYA1yJ+aQthI5qm4efiZ+1hLW8ms4lMYyYGowGD0VBgPBaAm4MbHg4euDu4l0tCFxERUfzCOj3UagyJ+evxObiCd2PL4PNCkqgck9n6aKs/lSSREkKUmDHPyJa4LQUSo/jMeOvzGT1m0LdBXwC2n9/OvN3zrqpDr+nR2ekKJF3PdHoGB50DAS4B+Lv44+Pig73d1d31vi6+1p4sIcRlSily8nLIMmWRacok05h5Ve+Tzk5n6aXN76mtyN6wYnFwBY9ASDsHKafBNwjsCr8L1Zw/PMlsw2FKlezTE0JUJKUUCmUdUH344mGOpRwjNSfV8pObSkpOCkfOH2H1xtXMvGkmYBnj8fBfDxdZb3xmvPV5mF8YE9tMxN/FH39Xf0uS5OpPLadaVw3kvjSRpRDi+szKTG5eLtmmbLLzssnJyyHTmGm9HH6Jpmk4651xtXfF3d4dJ72T9Y+XSsvVD3IMlvFSyTGW8VJKQW4GOFWucXySSAlRDSilyDJlkZKTQmpOKo56R5p4NgEgMSuRhfsXXk6OclKt5dJy01g0aJF1bMxPR3/im0PfFHqMrPjLy5s46hy5ucHNuDm4WS+vBbhaepH8XfwLTDwZ5h9GmH9Y+Z28ENWcWZkxmo3kmHKsCVOOKYecvJxCy+vt9LjYu+Cid8FZ74yT3qnqrZ14abxU4mHLeKnUWMhKtkzY6R8MlejOWUmkhKhkcvJySMlOITW3YNKTkpPC3a3vtg6inrltJlvjtlq3XzmTdP+G/fmg9wfW+r4++HWRx7tyhu42Pm0Y2GggXo5eeDp64ungiZeTF+eOnGNA+IAC+83uM/u/VQkhSkEphUmZMOYZyTXnFnzMy73mLPEOOgecdE446h1x1DnionepPtNz6PTg3cgyXioz0XInnzETDBcKvYPPViSREqIC7E3YS2JWYoFLZpeSoz71+1jnPVp/ej1T108tsp5bm9xqHTx9PuM8x1KOWbc56ZzwcPTAy9GrwADr2k61ebLDk5eTI0fPAonSlb90hzYdWmAOpksizkRYe7iEEMVnVmbyzHmYzCZMymR5zP8xmi8nTP+9HPdfejs9TnonHHWWhMlJ74QhxUD/PpblYM6fP49Op8PX1zJ2cNu2bTg4FH333Y4dO1i8eDFz584t9rk0atQId3d3NE3D29ubxYsX07Bhw2LvXyoOrpa79dJiwZQ/F1xGIrj5WeedAjh96hR9xo5k//79xa56woQJ3HrrrZYFlG+AJFJCXEeeOY/03HRSc1OtCQhYkqMNZzeQkpNCWk6apWcoP0FSSrFm1OUFYJ/b8BxnDWcLrd/fxd+avHg4eqC301uO4+B5VeJz5cDrx8IeY3K7ydZtRd255qR34t42Zb8chRA1WbIpGa88L1JzUi8nR/9JlK6c36w47HX2ONg54KBzwN7O3vpor7NH4+oxTd61vNmxyzKv4huvv4GbmxtPPvWkdXt2bjZ6feFf86FhoYSGhZY4xj//+hMfHx9en/Y6b0x/g08/+7RE+5eKSy3ISbeMl9J0oPIg/YJlQDpmKOSzqUiSSIkaSSnFX6f/Yv2Z9TjoHOjXwDK7taPOscB4otPppwvsN6jRIG5vdjsA285v48v9XxZ5jA1nN1jHJXQK6ETT7KZ4OnoSkxaDTtNZEyR/F3/+jf0XAF9nX3bdtQtN0zDkGohKiCpQZ/TFy0twtPVti4eDZdDlseRjnM88X2gcrvautPdrb3196ViFaerZlEA3y1pxV/Z4Hcw6iD624K+L7nW6WwesRsVHYTAaCq3Tz8WPFt6WSQsLO6crVeQ5FaYqnNPBrIMEGYKq1TlB1Wqns+lneTX2VeZ4zsE+vewuoxnzjBjzjKWaLT4xK5FMu0xG3TkKT29PovdF07ptawYNG8TMl2eSk5WDo7MjM+bOoHGzxmzbtI1F8xfx8TcfM3/WfOLOxnH21FnOx57nrgfv4q4H77o6PrORY8nHSLJLol5wPf6J/IfDFw9zMfEibzz9BnGxlnXynpvxHGFdwti+aTszX7LcoKJpGl/99hUaGlPumUJaimXm9SkvTKHv4L7Eno5l0phJtO/Snr079xIUHMSwO4Yx/535XEy8yDsL3iEkLIT5s+ZzJuYM8XHxnI89z8QpExl192iMVwyez8vL4/nnnyciIoKcnBweeeQRHnroIZRSTJkyhb///pvGjRtfdx3A4pJEStRIW89v5YmIJ6yvfzjyAwAtvFtwMvVkkWMSVsesZnXMagBGNB/Bw+0exsPRg/Wn17P1/NYCZR/56xHr86i7o9Dl3757x8o72Ju0t9D6hzcbzhvd3wDgrOEsk9dNLvIcvrnlG0J8QwD49tC3fH/k+0LLBXkH8cNtPxSIq6jLCC93eZkxLccAEHkuktciX7u8cV3BslF3R1kXxn1n2zvsTyq8S71Sn9N/VJVzqn22drU7p6rUTlPDrr78PnbV2CLrvhHfDfmuwGtN0wr0ThX2ecYcj+HzHz/HXm+PId3A1799jU6vIzIikg9nfMicRXOu3udYDIt+WUSGIYNbut7CmHvHYG9/dZKoaRp2mh2b1m+i7y2WKU5mvjSTeybdQ1jXMOLOxvHg/z3Ib5G/8dXHX/HKrFcI6xKGId2Ag5PlUtyHX32Im7sbyUnJ3DnoTvoO7oudZsfpk6eZvXA2TYKaMLb/WFb9uIqvV33N+tXr+d+c/zF38Vw04OjBI3z7x1IyM7MZ2XcUPfv3xE3zssb4xRdf4Onpyfbt28nJyaF79+4MGDCA3bt3c/jwYfbt28eFCxdo3bo1EydOLEWrFCSJlKiRgryDqOtWl1hDLGD5Cxegnns9nur4FC56F7wcvXjx3xdxtXe1/tK+Ut/6felVvxcADT0aFnsulra+bQvc1XalZl7NrM9d9a7WuApz5UzdTbyaFFn2v0ughNcJL/IvsSvHVvm5+FnrvHjxIrVq1Soylqp4TtdTWc/p4sWL1e6coGq1U896PZnXcB7+bv64OLgUK87S+u+M/K72rtZFqXPzconLsPQCOegccNQ5otfpuW3EbXg6exLgEkB8ZjxTJ03l0JFDlsWRjUbcHNyss6lfej7q9lG0q9MOAB9fH7JTsvGu613g2Jqm8eCIB0mIT8DPz4933nqHXH0uWzds5eTRk9ZymYZMyIEBvQcw9/W5jBs3ju4Du+NVywuj0cgHr37AtshtaHYa8efjIR2aeTejcePGDA4fTFxGHC1bt6Rn3564O7oT2jaUT979BDcHN7x1zvxf/1601+lICmxI5x6d2bdrH13Cwq3HX7t2LXv37uWHHyxJfGpqKkePHmXDhg3ccccd6HQ66tSpQ9++fcumkZRS5fYDDAIOA8eA5wvZrgFz87fvBcKuV2eHDh1UeVq/fn251i+KT9qi8pC2qDykLSqH9evXq4MHD9o6DKvXXntNvfvuu2r8+PFq+fLl1vfHjx+vPvzwQ6WUUidPnlQNGzZUSlniHzJkSIF9LwkODlYnT5686hgNGzZUCQkJKjMzU/3f//2feuKJJ5RSStWuXVtlZmYWGtfevXvVzJkzVd26dVV0dLT68ssv1f/93/+p3Nxca50nT55UJ0+eVMHBwQXivnQeV2577dVX1atPT1EqdpdS8YfU0P+7Tc1dPFdt3rvdWmbEiBFq9erVV8Xy2GOPqYULF1pfDx8+vMBndUlh7QrsUEXkJeU2sYSmaTpgPjAYaA3coWla6/8UGww0z/95EPikvOIRQgghaprU1FTq1q0LwKJFi8qkTmdnZ+bMmcPixYu5ePEiAwYM4KOPPrJuj4qKAuD48eOEhITw3HPP0bFjRw4dOkRqaip+fn7Y29uzfv16Tp06VbKDaxor/vyHbKMi6cI5tm/aRpv2bTBd0dM5cOBAPvnkE4xGyxCNI0eOkJGRQc+ePfnuu+/Iy8sjLi6O9evX3/BnAeV7aa8zcEwpdQJA07TvgNuBg1eUuR1YnJ/tbdE0zUvTtEClVFw5xiWEEELUCM8++yzjx4/ngw8+KLtLWUBgYCB33HEH8+fPZ+7cuTzyyCO0bdsWk8lEz549WbBgAXPmzGH9+vXodDpat27N4MGDSU9PZ+jQoXTs2JHQ0FBatmxZ4mN37tyFIROe5HTMCSY/9RB+AX4kn8u0br///vuJiYkhLCzMskC0ry+//PILw4cP5++//yYkJIQWLVrQq1evMvksNFVO69NomjYKGKSUuj//9d1AF6XUo1eUWQnMVEr9m//6L+A5pdSOourt2LGj2rGjyM03LCIigt69e5db/aL4pC0qD2mLykPaonKIiIjA39+fVq1a2TqUGmXatGm4ubnx9NNPQ2os0cZkzGh42HlRv1bdMjlGdHT0Ve2qadpOpVTHwsqXZ49UYRM7/DdrK04ZNE17EMulP/z9/Uu2knQJGQyGcq1fFJ+0ReUhbVF5SFtUDgaDAU9PT9LT020dSo2Sk5ODvb295XPX3HEwp5BtBw5mU5m1RXZ2don+j5VnInUWqH/F63rAuVKUQSn1GfAZWHqkyvOvMflrr/KQtqg8pC0qD2mLyiEiIgInJyfc3d1tHUqN8vbbbxd47ejclMz0VNy9fNHZlc2wbycnJ9q3b3/9gvnKcxXD7UBzTdMaa5rmAIwFfv1PmV+BezSLrkCqjI8SQgghRHE42Duhs3cpsySqNMqtR0opZdI07VFgDaADFiqlDmiaNil/+wLgd+AWLNMfZAKyjoUQQgghqoxynZBTKfU7lmTpyvcWXPFcAY/8dz8hhBBCiKrAdn1hQgghhBBVnCRSQgghRBWUlJREaGgooaGhBAQEULduXevr3Nzc6+4fERFBZGRkodsWLVqEr6+vda6n2bNnl3X4N2TChAnWJWCKIyYmhjZt2pRLLLLWnhBCCFEF1a5d2zqLeIH5lYopIiICNzc3wsPDC90+ZswYPvroI5KSkggKCmLUqFHUr1+/0LI1mfRICSGEENXEzp076dWrFx06dGDgwIHExVluhJ87dy6tW7embdu2jB07lpiYGBYsWMDs2bMJDQ1l48aNRdZZu3ZtmjVrZq1ryZIldO7cmdDQUB566CHy8vLIy8tjwoQJtGnThpCQEGsP1v/+9z86depEu3btGDlyJJmZlhnIJ0yYwOTJk+nTpw9NmjThn3/+YeLEibRq1YoJEyZYj+3m5sZTTz1FWFgY/fr1IyEh4ar4du/eXeg579y5k3bt2tGtWzfmz59fJp9vYaRHSgghhCgDIV+FFLnt1W6vMrrFaACWH1nOG5vfKLLsvvH7SnV8pRRTpkxhxYoV+Pr6smzZMl566SUWLlzIzJkzOXnyJI6OjqSkpODl5cWkSZOK1Yt1+vRpsrOzadu2LdHR0SxbtoxNmzZhb2/Pww8/zNKlSwkODiY2Npb9+/cDkJKSAsCIESN44IEHAHj55Zf54osvmDJlCgDJycn8/fff/PrrrwwdOpRNmzbx+eef06lTJ6KioggNDSUjI4OwsDDef/993njjDV5//fUC6/oZjUaeeeYZVq5cedU533vvvcybN49evXrxzDPPlOozLQ5JpIQQQohqICcnh/3799O/f38A8vLyCAwMBKBt27aMGzeOYcOGMWzYsGLVt2zZMtavX8/hw4f53//+h5OTE3/99Rc7d+6kU6dOAGRlZeHn58fQoUM5ceIEU6ZMYciQIQwYMACA/fv38/LLL5OSkoLBYGDgwIHW+ocOHYqmaYSEhODv709IiCURDQ4OJiYmhtDQUOzs7BgzZgwAd911FyNGjCgQ4+HDh4mOjr7qnFNTU0lJSbGup3f33Xfzxx9/lOZjvS5JpIQQQogyUNyepNEtRlt7p8qSUorg4GA2b9581bZVq1axYcMGfv31V6ZPn86BAweuW9+lMVKbN29myJAhDB48GKUU48ePv2qGcYA9e/awZs0a5s+fz/fff8/ChQuZMGECv/zyC+3atWPRokUFll5xdHQEwM7Ozvr80muTyVRoTJpWcGU5pRQtW7Zk27ZtBd5PSUm5qmx5kTFSQgghRDXg6OhIQkKCNZEyGo0cOHAAs9nMmTNn6NOnD7NmzbL2Drm7uxdrfbpu3bpx99138+GHH9KvXz9++OEH4uPjAbh48SKnTp0iMTERs9nMyJEjmT59Ort27QIgPT2dwMBAjEYjS5cuLfE5mc1m691533zzDT169CiwPSgoiMTExKvO2cvLC09PT/7991+AUh27uKRHSgghhKgG7Ozs+OGHH5g6dSqpqamYTCYef/xxWrRowV133UVqaipKKZ544gm8vLwYOnQoo0aNYsWKFcybN4+bbrqpyLqfe+45wsLCePHFF5kxYwYDBgzAbDZjb2/P/PnzcXZ25t5778VsNgOX18SbPn06Xbp0oWHDhoSEhJR4YWFXV1cOHDhAhw4d8PT0ZNmyZQW2Ozg48PXXX/Pcc88VOOfg4GC+/PJLJk6ciIuLS4FLimVNs0wuXnV07NhR7dixo9zqlwVBKw9pi8pD2qLykLaoHCIiIvD396dVq1a2DqVac3Nzw2AwXLNMenp6mS4eHR0dfVW7apq2UynVsbDycmlPCCGEEKKUJJESQgghRKV0vd6oykASKSGEEEKIUpJESgghhCilqjbOWFxbadpTEikhhBCiFJycnEhKSpJkqppQSpGUlISTk1OJ9pPpD4QQQohSqFevHmfPni10/TdRcbKzs0uc/BTFycmJevXqlWgfSaSEEEKIUrC3t6dx48a2DqPGi4iIoH379jY7vlzaE0IIIYQoJUmkhBBCCCFKSRIpIYQQQohSqnJLxGialgCcKsdD+ACJ5Vi/KD5pi8pD2qLykLaoHKQdKo+KaIuGSinfwjZUuUSqvGmatqOo9XRExZK2qDykLSoPaYvKQdqh8rB1W8ilPSGEEEKIUpJESgghhBCilCSRutpntg5AWElbVB7SFpWHtEXlIO1Qedi0LWSMlBBCCCFEKUmPlBBCCCFEKdXYRErTtEGaph3WNO2YpmnPF7Jd0zRtbv72vZqmhdkizpqgGG0xLr8N9mqaFqlpWjtbxFndXa8drijXSdO0PE3TRlVkfDVJcdpC07TemqZFaZp2QNO0fyo6xpqiGL+fPDVN+03TtD35bXGvLeKs7jRNW6hpWrymafuL2G6772ylVI37AXTAcaAJ4ADsAVr/p8wtwB+ABnQFtto67ur4U8y2CAe8858PlrawTTtcUe5v4HdglK3jro4/xfw/4QUcBBrkv/azddzV8aeYbfEi8E7+c1/gIuBg69ir2w/QEwgD9hex3Wbf2TW1R6ozcEwpdUIplQt8B9z+nzK3A4uVxRbAS9O0wIoOtAa4blsopSKVUsn5L7cAJVuaWxRHcf5PAEwBfgTiKzK4GqY4bXEn8JNS6jSAUkrao3wUpy0U4K5pmga4YUmkTBUbZvWnlNqA5bMtis2+s2tqIlUXOHPF67P575W0jLhxJf2c78PyV4coW9dtB03T6gLDgQUVGFdNVJz/Ey0Ab03TIjRN26lp2j0VFl3NUpy2+AhoBZwD9gGPKaXMFROeuILNvrP1FXGQSkgr5L3/3r5YnDLixhX7c9Y0rQ+WRKpHuUZUMxWnHeYAzyml8ix/fItyUpy20AMdgH6AM7BZ07QtSqkj5R1cDVOcthgIRAF9gabAn5qmbVRKpZVzbKIgm31n19RE6ixQ/4rX9bD8NVHSMuLGFetz1jStLfA5MFgplVRBsdUkxWmHjsB3+UmUD3CLpmkmpdQvFRJhzVHc30+JSqkMIEPTtA1AO0ASqbJVnLa4F5ipLAN1jmmadhJoCWyrmBBFPpt9Z9fUS3vbgeaapjXWNM0BGAv8+p8yvwL35N8J0BVIVUrFVXSgNcB120LTtAbAT8Dd8hd3ubluOyilGiulGimlGgE/AA9LElUuivP7aQVwk6Zpek3TXIAuQHQFx1kTFKctTmPpGUTTNH8gCDhRoVEKsOF3do3skVJKmTRNexRYg+WujIVKqQOapk3K374Ay11JtwDHgEwsf3WIMlbMtngVqA18nN8bYlKyWGiZKmY7iApQnLZQSkVrmrYa2AuYgc+VUoXeFi5Kr5j/L6YDizRN24fl8tJzSqlEmwVdTWma9i3QG/DRNO0s8BpgD7b/zpaZzYUQQgghSqmmXtoTQgghhLhhkkgJIYQQQpSSJFJCCCGEEKUkiZQQQgghRClJIiWEEEIIUUqSSAkhqhxN0/I0TYvSNG2/pmm/aZrmVcb1x2ia5pP/3FCWdQshqhdJpIQQVVGWUipUKdUGy0Kmj9g6ICFEzSSJlBCiqttM/uKkmqY11TRtdf5Cvhs1TWuZ/76/pmk/a5q2J/8nPP/9X/LLHtA07UEbnoMQooqqkTObCyGqB03TdFiW5/gi/63PgElKqaOapnUBPsaymOxc4B+l1PD8fdzyy09USl3UNM0Z2K5p2o+ylqMQoiQkkRJCVEXOmqZFAY2AncCfmqa5AeHA8vylhAAc8x/7AvcAKKXygNT896dqmjY8/3l9oDkgiZQQotgkkRJCVEVZSqlQTdM8gZVYxkgtAlKUUqHFqUDTtN7AzUA3pVSmpmkRgFN5BCuEqL5kjJQQospSSqUCU4GngSzgpKZpowHyV4Fvl1/0L2By/vs6TdM8AE8gOT+Jagl0rfATEEJUeZJICSGqNKXUbmAPMBYYB9ynadoe4ABwe36xx4A+mqbtw3IpMBhYDeg1TdsLTAe2VHTsQoiqT1NK2ToGIYQQQogqSXqkhBBCCCFKSRIpIYQQQohSkkRKCCGEEKKUJJESQgghhCglSaSEEEIIIUpJEikhhBBCiFKSREoIIYQQopQkkRJCCCGEKKX/Bz7tTCSQpwqUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_prc(\"Train Baseline\", train_labels, train_predictions_baseline, color=colors[0])\n",
    "plot_prc(\"Test Baseline\", test_labels, test_predictions_baseline, color=colors[0], linestyle='--')\n",
    "\n",
    "plot_prc(\"Train Weighted\", train_labels, train_predictions_weighted, color=colors[1])\n",
    "plot_prc(\"Test Weighted\", test_labels, test_predictions_weighted, color=colors[1], linestyle='--')\n",
    "\n",
    "plot_prc(\"Train Resampled\", train_labels, train_predictions_resampled, color=colors[2])\n",
    "plot_prc(\"Test Resampled\", test_labels, test_predictions_resampled, color=colors[2], linestyle='--')\n",
    "plt.legend(loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3o3f0ywl8uqW"
   },
   "source": [
    "## Applying this tutorial to your problem\n",
    "\n",
    "Imbalanced data classification is an inherently difficult task since there are so few samples to learn from. You should always start with the data first and do your best to collect as many samples as possible and give substantial thought to what features may be relevant so the model can get the most out of your minority class. At some point your model may struggle to improve and yield the results you want, so it is important to keep in mind the context of your problem and the trade offs between different types of errors."
   ]
  }
 ],
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